January 14, 2020
The Maxim Integrated AI project is comprised of four repositories:
- Start here: Top Level Documentation
- The software development kit (SDK), which contains drivers and example programs ready to run on the Evaluation Kit: MAX78000_SDK
- The training repository, which is used for deep learning model development and training: ai8x-training (described in this document)
- The synthesis repository, which is used to convert a trained model into C code using the “izer” tool: ai8x-synthesis (described in this document)
Open the .md version of this file in a markdown enabled viewer, for example Typora (http://typora.io).
See https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet for a description of Markdown. A PDF copy of this file is available in this repository. The GitHub rendering of this document does not show the formulas or the clickable table of contents.
[TOC]
This document covers several of Maxim’s ultra-low power machine learning accelerator systems. They are sometimes referred to by their die types. The following shows the die types and their corresponding part numbers:
| Die Type | Part Number(s) |
|---|---|
| AI84 | Unreleased test chip |
| AI85 | MAX78000 |
| AI87 | MAX78002 (in development) |
The following graphic shows an overview of the development flow:
Including the SDK, the expected/resulting file system layout will be:
..../ai8x-training/
..../ai8x-synthesis/
..../ai8x-synthesis/sdk/
..../manifold/
where “....” is the project root, for example ~/Documents/Source/AI.
This software currently supports Ubuntu Linux 18.04 LTS and 20.04 LTS. The server version is sufficient, see https://ubuntu.com/download/server. Alternatively, Ubuntu Linux can also be used inside the Windows Subsystem for Linux (WSL2) by following https://docs.nvidia.com/cuda/wsl-user-guide/. However, please note that WSL2 with CUDA is a pre-release and unexpected behavior may occur.
When going beyond simple models, model training does not work well without CUDA hardware acceleration. The network loader (“izer”) does not require CUDA, and very simple models can also be trained on systems without CUDA.
Recommendation: Install the latest version of CUDA 11 on Ubuntu 20.04 LTS. See https://developer.nvidia.com/cuda-toolkit-archive.
Note: When using multiple GPUs, the software will automatically use all available GPUs and distribute the workload. To prevent this, set the CUDA_VISIBLE_DEVICES environment variable. Use the --gpus command line argument to set the default GPU.
On a shared (multi-user) system that has previously been set up, only local installation is needed. CUDA and any apt-get or brew tasks are not necessary.
The screen command (or alternatively, the more powerful tmux) can be used inside a remote terminal to disconnect a session from the controlling terminal, so that a long running training session doesn’t abort due to network issues, or local power saving. In addition, screen can log all console output to a text file.
Example:
$ ssh targethost
targethost$ screen -L # or screen -r to resume, screen -list to list
targethost$
Ctrl+A,D to disconnectman screen and man tux describe the software in more detail.
The following software is optional, and can be replaced with other similar software of the user’s choosing.
- Visual Studio Code (Editor, Free), https://code.visualstudio.com, with the “Remote - SSH” plugin
- Typora (Markdown Editor, Free during beta), http://typora.io
- CoolTerm (Serial Terminal, Free), http://freeware.the-meiers.org or Serial ($30), https://apps.apple.com/us/app/serial/id877615577?mt=12
- Git Fork (Graphical Git Client, $50), https://git-fork.com or GitHub Desktop (Graphical Git Client, Free), https://desktop.github.com
- Beyond Compare (Diff and Merge Tool, $60), https://scootersoftware.com
The software in this project uses Python 3.8.6 or a later 3.8.x version.
It is not necessary to install Python 3.8.6 system-wide, or to rely on the system-provided Python. To manage Python versions, use pyenv (https://github.com/pyenv/pyenv).
On macOS (no CUDA support available):
$ brew install pyenv pyenv-virtualenv libomp libsndfile tcl-tkOn Linux:
$ sudo apt-get install -y make build-essential libssl-dev zlib1g-dev \
libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev \
libsndfile-dev portaudio19-dev
$ curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bashThen, add to either ~/.bash_profile, ~/.bashrc, or ~/.profile (as shown by the terminal output of the previous step):
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"If you use zsh as the shell (default on macOS), add these same commands to ~/.zshrc in addition.
Next, close the Terminal, open a new Terminal and install Python 3.8.6.
On macOS:
$ env \
PATH="$(brew --prefix tcl-tk)/bin:$PATH" \
LDFLAGS="-L$(brew --prefix tcl-tk)/lib" \
CPPFLAGS="-I$(brew --prefix tcl-tk)/include" \
PKG_CONFIG_PATH="$(brew --prefix tcl-tk)/lib/pkgconfig" \
CFLAGS="-I$(brew --prefix tcl-tk)/include" \
PYTHON_CONFIGURE_OPTS="--with-tcltk-includes='-I$(brew --prefix tcl-tk)/include' --with-tcltk-libs='-L$(brew --prefix tcl-tk)/lib -ltcl8.6 -ltk8.6'" \
pyenv install 3.8.6On Linux:
$ pyenv install 3.8.6If the local git environment has not been previously configured, add the following commands to configure e-mail and name. The e-mail must match GitHub (including upper/lower case):
$ git config --global user.email "[email protected]"
$ git config --global user.name "First Last"For convenience, define a shell variable named AI_PROJECT_ROOT:
$ export AI_PROJECT_ROOT="$HOME/Documents/Source/AI"Add this line to ~/.profile.
Nirvana Distiller is package for neural network compression and quantization. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller is automatically installed with the other packages.
Manifold is a model-agnostic visual debugging tool for machine learning. Manifold can compare models, detects which subset of data a model is inaccurately predicting, and explains the potential cause of poor model performance by surfacing the feature distribution difference between better and worse-performing subsets of data.
There is a hosted version of Manifold at http://manifold.mlvis.io/. To install it locally (for IP reasons and higher speed):
On macOS,
brew install yarn npmOn Linux,
$ cd $AI_PROJECT_ROOT
$ curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
$ echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
$ curl -sL https://deb.nodesource.com/setup_13.x | sudo -E bash -
$ sudo apt-get update
$ sudo apt-get install -y nodejs yarnOn both Mac and Linux:
$ git clone https://github.com/uber/manifold.git
$ cd manifold
$ yarn
# ignore warnings
$ cd examples/manifold
$ yarn
# ignore warningsWindows/MS-DOS is not supported for training networks at this time. This includes the Windows Subsystem for Linux (WSL) since it currently lacks CUDA support.
Change to the project root and run the following commands. Use your GitHub credentials when prompted.
$ cd $AI_PROJECT_ROOT
$ git clone https://github.com/MaximIntegratedAI/ai8x-training.git
$ git clone https://github.com/MaximIntegratedAI/ai8x-synthesis.gitTo create the virtual environment and install basic wheels:
$ cd ai8x-trainingIf you want to use the “develop” branch, switch to “develop” using this optional step:
$ git checkout develop # optionalThen continue with the following:
$ git submodule update --init
$ pyenv local 3.8.6
$ python3 -m venv .
$ source bin/activate
(ai8x-training) $ pip3 install -U pip wheel setuptoolsThe next step differs depending on whether the system uses Linux with CUDA 11.x, or any other setup.
For CUDA 11.x on Linux:
(ai8x-training) $ pip3 install -r requirements-cu111.txtFor all other systems, including CUDA 10.2 on Linux:
(ai8x-training) $ pip3 install -r requirements.txtBy default, the main branch is checked out. This branch has been tested more rigorously than the develop branch. develop, on the other hand, contains the latest improvements to the project. To switch to develop, use the following command:
(ai8x-training) $ git checkout developAfter additional testing, develop is merged into the main branch at regular intervals.
After a small delay of typically a day, a “Release” tag is created on GitHub for all non-trivial merges into the main branch. GitHub offers email alerts for all activity in a project, or for new releases only. Subscribing to releases only substantially reduces email traffic.
Note: Each “Release” automatically creates a code archive. It is recommended to use a git client to access (pull from) the main branch of the repository using a git client instead of downloading the archives.
In addition to code updated in the repository itself, submodules and Python libraries may have been updated as well.
Major upgrades (such as updating from PyTorch 1.5 to PyTorch 1.7) are best done by removing all installed wheels. This can be achieved most easily by creating a new folder and starting from scratch at [Upstream Code](#Upstream Code). Starting from scratch is also recommended when upgrading the Python version.
For minor updates, pull the latest code and install the updated wheels:
(ai8x-training) $ git pull
(ai8x-training) $ git submodule update --init
(ai8x-training) $ pip3 install -U pip setuptools
(ai8x-training) $ pip3 install -U -r requirements.txt # or requirements-cu111.txt with CUDA 11.xUpdating Python frequently requires updating pyenv first. Should pyenv install x.y.z
The ai8x-synthesis project does not require CUDA.
Start by deactivating the ai8x-training environment if it is active.
(ai8x-training) $ deactivateThen, create a second virtual environment:
$ cd $AI_PROJECT_ROOT
$ cd ai8x-synthesisIf you want to use the “develop” branch, switch to “develop” using this optional step:
$ git checkout develop # optionalThen continue:
$ git submodule update --init
$ pyenv local 3.8.6
$ python3 -m venv .
$ source bin/activate
(ai8x-synthesis) $ pip3 install -U pip setuptools
(ai8x-synthesis) $ pip3 install -r requirements.txtBranches and updates for ai8x-synthesis are handled similarly to the [ai8x-training](#Repository Branches) project.
The MAX78000 SDK is a git submodule of ai8x-synthesis. It is checked out automatically to a version compatible with the project into the folder sdk.
If the embedded C compiler is run on Windows instead of Linux, ignore this section and install the Maxim SDK executable, see https://github.com/MaximIntegratedAI/MaximAI_Documentation.
The Arm embedded compiler can be downloaded from https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-rm/downloads.
The RISC-V embedded compiler can be downloaded from https://github.com/xpack-dev-tools/riscv-none-embed-gcc-xpack/releases/.
Add the following to your ~/.profile, adjusting for the actual PATH to the compilers:
echo $PATH | grep -q -s "/usr/local/gcc-arm-none-eabi-9-2019-q4-major/bin"
if [ $? -eq 1 ] ; then
PATH=$PATH:/usr/local/gcc-arm-none-eabi-9-2019-q4-major/bin
export PATH
ARMGCC_DIR=/usr/local/gcc-arm-none-eabi-9-2019-q4-major
export ARMGCC_DIR
fi
echo $PATH | grep -q -s "/usr/local/riscv-none-embed-gcc/8.3.0-1.1/bin"
if [ $? -eq 1 ] ; then
PATH=$PATH:/usr/local/riscv-none-embed-gcc/8.3.0-1.1/bin
export PATH
RISCVGCC_DIR=/usr/local/riscv-none-embed-gcc/8.3.0-1.1
export RISCVGCC_DIR
fiThe debugger requires OpenOCD. On Windows, an OpenOCD executable is installed with the SDK. On macOS and Linux, the OpenOCD fork from https://github.com/MaximIntegratedMicros/openocd.git must be used. An Ubuntu Linux binary is available at https://github.com/MaximIntegratedAI/MAX78000_SDK/blob/master/Tools/OpenOCD/openocd. Note: A copy of the configuration files and a run-openocd-maxdap script are contained in the hardware folder of the ai8x-synthesis project.
gen-demos-max78000.sh will create code that is compatible with the SDK and copy it into the SDK’s Example directories.
MAX78000/MAX78002 are embedded accelerators. Unlike GPUs, MAX78000/MAX78002 do not have gigabytes of memory, and cannot support arbitrary data (image) sizes.
A typical CNN operation consists of pooling followed by a convolution. While these are traditionally expressed as separate layers, pooling can be done “in-flight” on MAX78000/MAX78002 for greater efficiency.
To minimize data movement, the accelerator is optimized for convolutions with in-flight pooling on a sequence of layers. MAX78000 and MAX78002 also support in-flight element-wise operations, pass-through layers and 1D convolutions (without element-wise operations):
The MAX78000/MAX78002 accelerators contain 64 parallel processors. There are four groups that contain 16 processors each.
Each processor includes a pooling unit and a convolutional engine with dedicated weight memory:
Data is read from data memory associated with the processor, and written out to any data memory located within the accelerator. To run a deep convolutional neural network, multiple layers are chained together, where each layer’s operation is individually configurable. The output data from one layer is used as the input data for the next layer, for up to 32 layers (where in-flight pooling and in-flight element-wise operations do not count as layers).
The following picture shows an example view of a 2D convolution with pooling:
Data memory, weight memory, and processors are interdependent.
In the MAX78000/MAX78002 accelerator, processors are organized as follows:
- Each processor is connected to its own dedicated weight memory instance.
- Four processors share one data memory instance.
- A group of sixteen processors shares certain common controls and can be operated as a slave to another group, or independently/separately.
Any given processor has visibility of:
- Its dedicated weight memory, and
- The data memory instance it shares with three other processors.
For each of the four 16-processor groups, weight memory and processors can be visualized as follows. Assuming one input channel processed by processor 0, and 8 output channels, the 8 shaded kernels will be used:
Data memory connections can be visualized as follows:
All input data must be located in the data memory instance the processor can access. Conversely, output data can be written to any data memory instance inside the accelerator (but not to general purpose SRAM on the Arm microcontroller bus).
The data memory instances inside the accelerator are single-port memories. This means that only one access operation can happen per clock cycle. When using the HWC data format (see Channel Data Formats), this means that each of the four processors sharing the data memory instance will receive one byte of data per clock cycle (since each 32-bit data word consists of four packed channels).
When data has more channels than active processors, “multi-pass” is used. Each processor works on more than one channel, using multiple sequential passes, and each data memory holds more than four channels.
As data is read using multiple passes, and all available processor work in parallel, the first pass reads channels 0 through 63, the second pass reads channels 64 through 127, etc., assuming 64 processors are active.
For example, if 192-channel data is read using 64 active processors, Data Memory 0 stores three 32-bit words: channels 0, 1, 2, 3 in the first word, 64, 65, 66, 67 in the second word, and 128, 129, 130, 131 in the third word. Data Memory 1 stores channels 4, 5, 6, 7 in the first word, 68, 69, 70, 71 in the second word, and 132, 133, 134, 135 in the third word, and so on. The first processor processes channel 0 in the first pass, channel 64 in the second pass, and channel 128 in the third pass.
Note: Multi-pass also works with channel counts that are not a multiple of 64, and can be used with less than 64 active processors.
The machine also implements a streaming mode. Streaming allows input data dimensions that exceed the available per-channel data memory in the accelerator.
The following illustration shows the basic principle: In order to produce the first output pixel of the second layer, not all data needs to be present at the input. In the example, a 5×5 input needs to be available.
In the accelerator implementation, data is shifted into the Tornado memory in a sequential fashion, so prior rows will be available as well. In order to produce the blue output pixel, input data up to the blue input pixel must be available.
When the yellow output pixel is produced, the first (black) pixel of the input data is no longer needed and its data can be discarded:
The number of discarded pixels is network specific and dependent on pooling strides and the types of convolution. In general, streaming mode is only useful for networks where the output data dimensions decrease from layer to layer (for example, by using a pooling stride).
Note: Streaming mode requires the use of FIFOs.
Since the data memory instances are single-port memories, software would have to temporarily disable the accelerator in order to feed it new data. Using FIFOs, software can input available data while the accelerator is running. The accelerator will autonomously fetch data from the FIFOs when needed, and stall (pause) when no enough data is available.
The MAX78000/MAX78002 accelerator has two types of FIFO:
There are four dedicated FIFOs connected to processors 0-3, 16-19, 32-35, and 48-51, supporting up to 16 input channels (in HWC format) or four channels (CHW format). These FIFOs work when used from the ARM Cortex-M4 core and from the RISC-V core.
The standard FIFOs are selected using the --fifo argument for ai8xize.py.
The fast FIFO is only available from the RISC-V core, and runs synchronously with the RISC-V for increased throughput. It supports up to four input channels (HWC format) or a single channel (CHW format). The fast FIFO is connected to processors 0, 1, 2, 3 or 0, 16, 32, 48.
The fast FIFO is selected using the --fast-fifo argument for ai8xize.py.
All weights, bias values and data are stored and computed in Q7 format (signed two’s complement 8-bit integers, [–128...+127]). See https://en.wikipedia.org/wiki/Q_%28number_format%29.
The 8-bit value
Examples:
| Binary | Value |
|---|---|
| 0000 0000 | 0 |
| 0000 0001 | 1/128 |
| 0000 0010 | 2/128 |
| 0111 1110 | 126/128 |
| 0111 1111 | 127/128 |
| 1000 0000 | −128/128 (–1) |
| 1000 0001 | −127/128 |
| 1000 0010 | −126/128 |
| 1111 1110 | −2/128 |
| 1111 1111 | −1/128 |
On MAX78000/MAX78002, weights can be 1, 2, 4, or 8 bits wide (configurable per layer using the quantization key). Bias values are always 8 bits wide. Data is 8 bits wide, except for the last layer that can optionally output 32 bits of unclipped data in Q17.14 format when not using activation.
| wt bits | min | max |
|---|---|---|
| 8 | –128 | +127 |
| 4 | –8 | 7 |
| 2 | –2 | 1 |
| 1 | –1 | 0 |
Note that 1-bit weights (and, to a lesser degree, 2-bit weights) require the use of bias to produce useful results. Without bias, all sums of products of activated data from a prior layer would be negative, and activation of that data would always be zero.
In other cases, using bias in convolutional layers does not improve inference performance. In particular, Quantization-Aware Training (QAT) optimizes the weight distribution, possibly deteriorating the distribution of the bias values.
MAX78000/MAX78002 rounding (for the CNN sum of products) uses “round half towards positive infinity”, i.e. y = np.floor(0.5 + x) and in Excel as =FLOOR.PRECISE(0.5 + X).
By way of example:
| Input | Rounded |
|---|---|
| +3.5 | +4 |
| +3.25, +3.0, +2.75, +2.5 | +3 |
| +2.25, +2.0, +1.75, +1.5 | +2 |
| +1.25, +1.0, +0.75, +0.5 | +1 |
| +0.25, 0, –0.25, –0.5 | 0 |
| –0.75, –1.0, –1.25, –1.5 | –1 |
| –1.75, –2.0, –2.25, –2.5 | –2 |
| –2.75, –3.0, –3.25, –3.5 | –3 |
Addition works similarly to regular two’s-complement arithmetic.
Example: $$ w_0 = 1/64 → 00000010 $$ $$ w_1 = 1/2 → 01000000 $$ $$ w_0 + w_1 = 33/64 → 01000010 $$
Values smaller than
The MAX78000/MAX78002 CNN sum of products uses full resolution for both products and sums, so the saturation happens only at the very end of the computation.
Example 1:
Example 2:
Since operand values are implicitly divided by 128, the product of two values has to be shifted in order to maintain magnitude when using a standard multiplier (e.g., 8×8):
In software,
- Determine the sign bit:
$s = sign(w_0) * sign(w_1)$ - Convert operands to absolute values:
$w'_0 = abs(w_0); w'_1 = abs(w_1)$ - Multiply using standard multiplier:
$w'_0 * w'_1 = w''_0/128 * w''_1/128; r' = w''_0 * w''_1$ - Shift:
$r'' = r' ≫ 7$ - Round up/down depending on
$r'[6]$ - Apply sign:
$r = s * r''$
Example 1:
A “standard” two’s-complement multiplication would return 00000000 10000000. The MAX78000/MAX78002 data format discards the rightmost bits.
Example 2:
“Standard” two’s-complement multiplication would return 00000000 01000000, the MAX78000/MAX78002 result is truncated to 0 after the shift operation.
Operations preserve the sign bit.
Example 1:
- Determine the sign bit:
$s = sign(-1/64) * sign(1/4) = -1 * 1 = -1$ - Convert operands to absolute values:
$w'_0 = abs(-1/64); w'_1 = abs(1/4)$ - Multiply using standard multiplier:
$r' = 1/64 ≪ 7 * 1/4 ≪ 7 = 2 * 32 = 64$ - Shift:
$r'' = r' ≫ 7 = 64 ≫ 7 = 0$ - Apply sign:
$r = s * r'' = -1 * 0 = 0$
Example 2:
- Determine the sign bit:
$s = sign(-1/64) * sign(1/2) = -1 * 1 = -1$ - Convert operands to absolute values:
$w'_0 = abs(-1/64); w'_1 = abs(1/2)$ - Multiply using standard multiplier:
$r' = 1/64 ≪ 7 * 1/2 ≪ 7 = 2 * 64 = 128$ - Shift:
$r'' = r' ≫ 7 = 128 ≫ 7 = 1$ - Apply sign:
$r = s * r'' = -1 * 1 ≫ 7 = -1/128$
Example 3:
All internal data are stored in HWC format, 4 channels per 32-bit word. Assuming 3-color (or 3-channel) input, one byte will be unused. Example:
The input layer can alternatively also use the CHW format (sequence of channels), for example:
The accelerator supports both HWC and CHW input formats to avoid unnecessary data manipulation. Internal layers always use the HWC format.
In general, HWC is faster since each memory read can deliver data to up to four processors in parallel. On the other hand, four processors must share one data memory instance, which reduces the maximum allowable dimensions.
When using the CHW data format, only one of the four processors sharing the data memory instance can be used. The next channel needs to use a processor connected to a different data memory instance, so that the machine can deliver one byte per clock cycle to each enabled processor.
Because of the fact that a processor has its own dedicated weight memory, this will introduce “gaps” in the weight memory map, as shown in the following illustration:
For each layer, a set of active processors must be specified. The number input channels for the layer must be equal to or a multiple of the active processors, and the input data for that layer must be located in data memory instances accessible to the selected processors.
It is possible to specify a relative offset into the data memory instance that applies to all processors. Example: Assuming HWC data format, specifying the offset as 8192 bytes will cause processors 0-3 to read their input from the second half of data memory 0, processors 4-7 will read from the second half of data memory instance 1, etc.
For most simple networks with limited data sizes, it is easiest to ping-pong between the first and second halves of the data memories - specify the data offset as 0 for the first layer, 0x2000 for the second layer, 0 for the third layer, etc. This strategy avoids overlapping inputs and outputs when a given processor is used in two consecutive layers.
Even though it is supported by the accelerator, the Network Generator will not be able to check for inadvertent overwriting of unprocessed input data by newly generated output data when overlapping data or streaming data. Use the --overlap-data command line switch to disable these checks, and to allow overlapped data.
For each layer, the weight memory start column is automatically configured by the Network Loader. The start column must be a multiple of 4, and the value applies to all processors.
The following example shows the weight memory layout for two layers. The first layer (L0) has 8 inputs and 10 outputs, and the second layer (L1) has 10 inputs and 2 outputs.
The file ai84net.xlsx contains an example for a single-channel CHW input using the AI84Net5 network (this example also supports up to four channels in HWC).
Note: As described above, multiple CHW channels must be loaded into separate memory instances. When using a large number of channels, this can cause “holes” in the processor map, which in turn can cause subsequent layers’ kernels to require padding.
The Network Loader prints a kernel map that shows the kernel arrangement based on the provided network description. It will also flag cases where kernel or bias memories are exceeded.
The following picture shows an example of a Conv2d with 1×1 kernels, 5 input channels, 2 output channels and data size of 2×2. The inputs are shown on the left, and the outputs on the right, and the kernels are shown lined up with the associated inputs — the number of kernel rows matches the number of input channels, and the number kernel columns matches the number of output channels. The lower half of the picture shows how the data is arranged in memory when HWC data is used for both input and output.
The MAX78000 hardware does not support arbitrary network parameters. Specifically,
-
Conv2d:- Kernel sizes must be 1×1 or 3×3.
- Padding can be 0, 1, or 2. Padding always uses zeros.
- Stride is fixed to [1, 1].
-
Conv1d:- Kernel sizes must be 1 through 9.
- Padding can be 0, 1, or 2.
- Stride is fixed to 1.
-
ConvTranspose2d:- Kernel sizes must be 3×3.
- Padding can be 0, 1, or 2.
- Stride is fixed to [2, 2]. Output padding is fixed to 1.
-
A programmable layer-specific shift operator is available at the output of a convolution, see [
output_shift(Optional)](#output_shift (Optional)). -
The supported activation functions are
ReLUandAbs, and a limited subset ofLinear. -
Pooling:
-
Both max pooling and average pooling are available, with or without convolution.
-
Pooling does not support padding.
-
Pooling strides can be 1 through 16. For 2D pooling, the stride is the same for both dimensions.
-
For 2D pooling, supported pooling kernel sizes are 1×1 through 16×16, including non-square kernels. 1D pooling supports kernel sizes from 1 through 16. Note: Pooling kernel size values do not have to be the same as the pooling stride.
-
Average pooling is implemented both using
floor()and using rounding (half towards positive infinity). Use the--avg-pool-roundingswitch to turn on rounding in the training software and the Network Generator.Example:
-
floor: Since there is a quantization step at the output of the average pooling, a 2×2
AvgPool2dof[[0, 0], [0, 3]]will return$\lfloor \frac{3}{4} \rfloor = 0$ . -
rounding: 2×2
AvgPool2dof[[0, 0], [0, 3]]will return$\lfloor \frac{3}{4} \rceil = 1$ .
-
floor: Since there is a quantization step at the output of the average pooling, a 2×2
-
-
The number of input channels must not exceed 1024 per layer.
-
The number of output channels must not exceed 1024 per layer.
-
The number of layers must not exceed 32 (where pooling and element-wise operations do not add to the count when preceding a convolution).
-
The maximum dimension (number of rows or columns) for input or output data is 1023.
- When using data greater than 90×91,
streamingmode must be used. - When using
streamingmode, the product of any layer’s input width, input height, and input channels divided by 64 rounded up must not exceed 2^21:$width * height * ⌈\frac{channels}{64}⌉ < 2^{21}$ . width and height must not exceed 1023. - Streaming is limited to 8 layers or less, and is limited to four FIFOs (up to 4 input channels in CHW and up to 16 channels in HWC format), see FIFOs.
- When using data greater than 90×91,
-
The weight memory supports up to 768 * 64 3×3 Q7 kernels (see Number Format). When using 1-, 2- or 4 bit weights, the capacity increases accordingly. When using more than 64 input or output channels, weight memory is shared and effective capacity decreases. Weights must be arranged according to specific rules detailed in [Layers and Weight Memory](#Layers and Weight Memory).
-
There are 16 instances of 32 KiB data memory. When not using streaming mode, any data channel (input, intermediate, or output) must completely fit into one memory instance. This limits the first-layer input to 181×181 pixels per channel in the CHW format. However, when using more than one input channel, the HWC format may be preferred, and all layer output are in HWC format as well. In those cases, it is required that four channels fit into a single memory instance -- or 91×90 pixels per channel. Note that the first layer commonly creates a wide expansion (i.e., large number of output channels) that needs to fit into data memory, so the input size limit is mostly theoretical.
-
The hardware supports 1D and 2D convolution layers, 2D transposed convolution layers (upsampling), element-wise addition, subtraction, binary OR, binary XOR as well as fully connected layers (
Linear) (implemented using 1×1 convolutions on 1×1 data):-
The maximum number of input neurons is 1024, and the maximum number of output neurons is 1024 (16 each per processor used).
-
Flattenfunctionality is available to convert 2D input data for use by fully connected layers, see [Fully Connected Layers](#Fully Connected (Linear) Layers). -
When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy H×W ≤ 256 and C ≤ 64. Pooling cannot be used at the same time as flattening.
-
Element-wise operators support from 2 up to 16 inputs.
-
Element-wise operators can be chained in-flight with pooling and 2D convolution (where the order of pooling and element-wise operations can be swapped).
-
For convenience, a
Softmaxoperator is supported in software.
-
-
Since the internal network format is HWC in groups of four channels, output concatenation only works properly when all components of the concatenation other than the last have multiples of four channels.
-
Dilation, groups, and depth-wise convolutions are not supported. Note: Batch normalization should be folded into the weights, see Batch Normalization.
m×n fully connected layers can be realized in hardware by “flattening” 2D input data of dimensions C×H×W into m=C×H×W channels of 1×1 input data. The hardware will produce n channels of 1×1 output data. When chaining multiple fully connected layers, the flattening step is omitted. The following picture shows 2D data, the equivalent flattened 1D data, and the output.
For MAX78000/MAX78002, the product H×W must not exceed 256, and C must not exceed 64.
The hardware supports 2D upsampling (“fractionally-strided convolutions”, sometimes called “deconvolution” even though this is not strictly mathematically correct). The PyTorch equivalent is ConvTranspose2D with a stride of 2.
The example shows a fractionally-strided convolution with a stride of 2, pad of 1, and a 3×3 kernel. This “upsamples” the input dimensions from 3×3 to output dimensions of 6×6.
The main training software is train.py. It drives the training aspects including model creation, checkpointing, model save, and status display (see --help for the many supported options, and the scripts/train_*.sh scripts for example usage).
The ai84net.py and ai85net.py files contain models that fit into AI84’s weight memory. These models rely on the MAX78000/MAX78002 hardware operators that are defined in ai8x.py.
To train the FP32 model for MNIST on MAX78000, run scripts/train_mnist.sh from the ai8x-training project. This script will place checkpoint files into the log directory. Training makes use of the Distiller framework, but the train.py software has been modified slightly to improve it and add some MAX78000/MAX78002 specifics.
The following table describes the most important command line arguments for train.py. Use --help for a complete list.
| Argument | Description | Example |
|---|---|---|
--help |
Complete list of arguments | |
| Device selection | ||
--device |
Set device (default: AI84) | --device MAX78000 |
| Model and dataset | ||
-a, --arch |
Set model (collected from models folder) | --model ai85net5 |
--dataset |
Set dataset (collected from datasets folder) | --dataset MNIST |
--data |
Path to dataset (default: data) | --data /data/ml |
| Training | ||
--epochs |
Number of epochs to train (default: 90) | --epochs 100 |
-b, --batch-size |
Mini-batch size (default: 256) | --batch-size 512 |
--compress |
Set compression and learning rate schedule | --compress schedule.yaml |
--lr, --learning-rate |
Set initial learning rate | --lr 0.001 |
--deterministic |
Seed random number generators with fixed values | |
--resume-from |
Resume from previous checkpoint | --resume-from chk.pth.tar |
--qat-policy |
Define QAT policy in YAML file (default: qat_policy.yaml). Use ‘’None” to disable QAT. | --qat-policy qat_policy.yaml |
| Display and statistics | ||
--confusion |
Display the confusion matrix | |
--param-hist |
Collect parameter statistics | |
--pr-curves |
Generate precision-recall curves | |
--embedding |
Display embedding (using projector) | |
| Hardware | ||
--use-bias |
The bias=True parameter is passed to the model. The effect of this parameter is model dependent (the parameter is either ignored, effective for some operations, or all operations). |
|
--avg-pool-rounding |
Use rounding for AvgPool | |
| Evaluation | ||
-e, --evaluate |
Evaluate previously trained model | |
--8-bit-mode, -8 |
Simluate quantized operation for hardware device (8-bit data) | |
--exp-load-weights-from |
Load weights from file | |
| Export | ||
--summary onnx |
Export trained model to ONNX (default name: to model.onnx) | |
--summary onnx_simplified |
Export trained model to simplified ONNX file (default name: model.onnx) | |
--summary-filename |
Change the file name for the exported model | --summary-filename mnist.onnx |
--save-sample |
Save data[index] from the test set to a NumPy pickle for use as sample data | --save-sample 10 |
nvidia-smi can be used in a different terminal during training to examine the GPU resource usage of the training process. In the following example, the GPU is using 100% of its compute capabilities, but not all of the available memory. In this particular case, the batch size could be increased to use more memory.
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50 Driver Version: 430.50 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 208... Off | 00000000:01:00.0 On | N/A |
| 39% 65C P2 152W / 250W | 3555MiB / 11016MiB | 100% Default |
+-------------------------------+----------------------+----------------------+
...
The ai8x.py file contains customized PyTorch classes (subclasses of torch.nn.Module). Any model that is designed to run on MAX78000/MAX78002 should use these classes. There are three main changes over the default classes in torch.nn.Module:
- Additional “Fused” operators that model in-flight pooling and activation.
- Rounding and clipping that matches the hardware.
- Support for quantized operation (when using the
-8command line argument).
The following modules are predefined:
| Name | Description / PyTorch equivalent |
|---|---|
| Conv2d | Conv2d |
| FusedConv2dReLU | Conv2d, followed by ReLU |
| FusedConv2dAbs | Conv2d, followed by Abs |
| MaxPool2d | MaxPool2d |
| FusedMaxPoolConv2d | MaxPool2d, followed by Conv2d |
| FusedMaxPoolConv2dReLU | MaxPool2d, followed by Conv2d, and ReLU |
| FusedMaxPoolConv2dAbs | MaxPool2d, followed by Conv2d, and Abs |
| AvgPool2d | AvgPool2d |
| FusedAvgPoolConv2d | AvgPool2d, followed by Conv2d |
| FusedAvgPoolConv2dReLU | AvgPool2d, followed by Conv2d, and ReLU |
| FusedAvgPoolConv2dAbs | AvgPool2d, followed by Conv2d, and Abs |
| ConvTranspose2d | ConvTranspose2d |
| FusedConvTranspose2dReLU | ConvTranspose2d, followed by ReLU |
| FusedConvTranspose2dAbs | ConvTranspose2d, followed by Abs |
| FusedMaxPoolConvTranspose2d | MaxPool2d, followed by ConvTranspose2d |
| FusedMaxPoolConvTranspose2dReLU | MaxPool2d, followed by ConvTranspose2d, and ReLU |
| FusedMaxPoolConvTranspose2dAbs | MaxPool2d, followed by ConvTranspose2d, and Abs |
| FusedAvgPoolConvTranspose2d | AvgPool2d, followed by ConvTranspose2d |
| FusedAvgPoolConvTranspose2dReLU | AvgPool2d, followed by ConvTranspose2d, and ReLU |
| FusedAvgPoolConvTranspose2dAbs | AvgPool2d, followed by ConvTranspose2d, and Abs |
| Linear | Linear |
| FusedLinearReLU | Linear, followed by ReLU |
| FusedLinearAbs | Linear, followed by Abs |
| Conv1d | Conv1d |
| FusedConv1dReLU | Conv1d, followed by ReLU |
| FusedConv1dAbs | Conv1d, followed by Abs |
| MaxPool1d | MaxPool1d |
| FusedMaxPoolConv1d | MaxPool1d, followed by Conv1d |
| FusedMaxPoolConv1dReLU | MaxPool2d, followed by Conv1d, and ReLU |
| FusedMaxPoolConv1dAbs | MaxPool2d, followed by Conv1d, and Abs |
| AvgPool1d | AvgPool1d |
| FusedAvgPoolConv1d | AvgPool1d, followed by Conv1d |
| FusedAvgPoolConv1dReLU | AvgPool1d, followed by Conv1d, and ReLU |
| FusedAvgPoolConv1dAbs | AvgPool1d, followed by Conv1d, and Abs |
| Add | Element-wise Add |
| Sub | Element-wise Sub |
| Or | Element-wise bitwise Or |
| Xor | Element-wise bitwise Xor |
Dropout modules such as torch.nn.Dropout() and torch.nn.Dropout2d()are automatically disabled during inference, and can therefore be used for training without affecting inference.
There are two supported cases for view() or reshape().
- Conversion between 1D data and 2D data: Both the batch dimension (first dimension) and the channel dimension (second dimension) must stay the same. The height/width of the 2D data must match the length of the 1D data (i.e., H×W = L).
Examples:
x = x.view(x.size(0), x.size(1), -1) # 2D to 1Dx = x.view(x.shape[0], x.shape[1], 16, -1) # 1D to 2DNote:x.size()andx.shape[]are equivalent. When reshaping data,in_dim:must be specified in the model description file. - Conversion from 1D and 2D to Fully Connected (“flattening”): The batch dimension (first dimension) must stay the same, and the other dimensions are combined (i.e., M = C×H×W or M = C×L).
Example:
x = x.view(x.size(0), -1) # Flatten
The hardware always uses signed integers for data and weights. While data is always 8-bit, weights can be configured on a per-layer basis. However, training makes use of floating point values for both data and weights, while also clipping (clamping) values.
When using the -8 command line switch, all module outputs are quantized to 8-bit in the range [-128...+127] to simulate hardware behavior. The last layer can optionally use 32-bit output for increased precision. This is simulated by adding the parameter wide=True to the module function call.
Quantization-aware training (QAT) is enabled by default. QAT is controlled by a policy file, specified by --qat-policy.
- After
start_epochepochs, training will learn an additional parameter that corresponds to a shift of the final sum of products. weight_bitsdescribes the number of bits available for weights.overridesallows specifying theweight_bitson a per-layer basis.
By default, weights are quantized to 8-bits after 10 epochs as specified in qat_policy.yaml. A more refined example that specifies weight sizes for individual layers can be seen in qat_policy_cifar100.yaml.
Quantization-aware training can be disabled by specifying --qat-policy None.
Batch normalization after Conv1d and Conv2d layers is supported using “fusing”. The fusing operation merges the effect of batch normalization layers into the parameters of the preceding convolutional layer. For detailed information about batch normalization fusing/folding, see Section 3.2 of the following paper: https://arxiv.org/pdf/1712.05877.pdf.
After fusing/folding, the network will not contain any batchnorm layers. The effects of batch normalization will instead be expressed by modified weights and biases of the preceding convolutional layer. If the trained network contains batchnorm layers, the batchnormfuser.py script (see BatchNorm Fusing) should be called before quantize.py to fuse the batchnorm layers. To be able perform folding/fusing by running batchnormfuser.py, a second model architecture should be defined without batchnorm layers. This architecture should be exactly the same as the input model architecture, except for the removal of all batchnorm layers.
Both TensorBoard and Manifold can be used for model comparison and feature attribution.
TensorBoard is built into train.py. It provides a local web server that can be started before, during, or after training and it picks up all data that is written to the logs/ directory.
For classification models, TensorBoard supports the optional --param-hist and --embedding command line arguments. --embedding randomly selects up to 100 data points from the last batch of each verification epoch. These can be viewed in the “projector” tab in TensorBoard.
To start the TensorBoard server, use a second terminal window:
(ai8x-training) $ tensorboard --logdir='./logs'
TensorBoard 2.2.2 at http://127.0.0.1:6006/ (Press CTRL+C to quit)On a shared system, add the --port 0 command line option.
The training progress can be observed by starting TensorBoard and pointing a web browser to the port indicated.
When using a remote system, use ssh in another terminal window to forward the remote port to the local machine:
$ ssh -L 6006:127.0.0.1:6006 targethostWhen using PuTTY, port forwarding is achieved as follows:
The quickest way to integrate manifold is by creating CSV files from the training software. Note that performance will suffer when there are more than about 20,000 records in the CSV file. Subsampling the data is one way to avoid this problem.
The train.py program can create CSV files using the --save-csv command line argument in combination with --evaluate:
./train.py --model ai85net5 --dataset MNIST --confusion --evaluate --save-csv mnist --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-mnist.pth.tar -8To run the manifold example application:
$ cd manifold/examples/manifold
$ npm run startThe code will run in JavaScript inside the browser (this may cause warnings that the web page is consuming lots of resources). To run a browser remotely on a development machine, forward X11 using the following command:
$ ssh -Yn targethost firefox http://localhost:8080/To forward only the remote web port, use ssh:
$ ssh -L 8080:127.0.0.1:8080 targethostThe training software integrates code to generate SHAP plots (see https://github.com/slundberg/shap). This can help with feature attribution for input images.
The train.py program can create plots using the --shap command line argument in combination with --evaluate:
./train.py --model ai85net5 --dataset CIFAR10 --confusion --evaluate --device MAX78000 --exp-load-weights-from logs/CIFAR-new/best.pth.tar --shap 3This will create a plot with a random selection of 3 test images. The plot shows ten outputs (the ten classes) for the three different input images on the left. Red pixels increase the model’s output while blue pixels decrease the output. The sum of the SHAP values equals the difference between the expected model output (averaged over the background dataset) and the current model output.
If batchnorm fusing is needed (see Batch Normalization), the batchnormfuser.py tool must be run.
The following table describes the command line arguments for batchnormfuser.py:
| Argument | Description | Example |
|---|---|---|
-i, --inp_path |
Set input checkpoint path | -i logs/2020.06.05-235316/best.pth.tar |
-o, --out_path |
Set output checkpoint path for saving fused model | -o best_without_bn.pth.tar |
-oa, --out_arch |
Set output architecture name (architecture without batchnorm layers) | -oa ai85simplenet |
There are two main approaches to quantization — quantization-aware training and post-training quantization. The MAX78000/MAX78002 support both approaches.
The quantize.py software quantizes an existing PyTorch checkpoint file and writes out a new PyTorch checkpoint file that can then be used to evaluate the quality of the quantized network, using the same PyTorch framework used for training. The same new checkpoint file will also be used to feed the Network Loader.
Quantization-aware training is the better performing approach. It is enabled by default. QAT learns additional parameters during training that help with quantization (see [Weights: Quantization Aware Training (QAT)](#Weights: Quantization Aware Training (QAT)). No additional arguments are needed for quantize.py.
This approach is also called ”naive quantization”. It should be used when --qat-policy None is specified for training.
While several approaches for clipping are implemented in quantize.py, clipping with a simple fixed scale factor performs best, based on experimental results. The approach requires the clamping operators implemented in ai8x.py.
Note that the “optimum” scale factor for simple clipping is highly dependent on the model and weight data. For the MNIST example, a --scale 0.85 works well. For the CIFAR-100 example on the other hand, Top-1 performance is 30 points better with --scale 1.0.
The quantize.py software has the following important command line arguments:
| Argument | Description | Example |
|---|---|---|
--help |
Complete list of options | |
| Device selection | ||
--device |
Set device (default: AI84) | --device MAX78000 |
| Debug | ||
-v |
Verbose output | |
| Weight quantization | ||
-c, --config-file |
YAML file with weight quantization information (default: from checkpoint file, or 8-bit for all layers) |
-c mnist.yaml |
--clip-method |
Non-QAT clipping method — either STDDEV, AVG, AVGMAX or SCALE | --clip-method SCALE |
--scale |
Sets scale for the SCALE clipping method | --scale 0.85 |
Note: The syntax for the optional YAML file is described below. The same file can be used for both quantize.py and ai8xize.py.
Copy the working and tested weight files into the trained/ folder of the ai8x-synthesis project.
Example for MNIST:
(ai8x-synthesis) $ scripts/quantize_mnist.shTo evaluate the quantized network for MAX78000 (run from the training project):
(ai8x-training) $ scripts/evaluate_mnist.shIf quantization-aware training is not desired, post-training quantization can be improved using more sophisticated methods. For example, see https://github.com/pytorch/glow/blob/master/docs/Quantization.md, https://github.com/ARM-software/ML-examples/tree/master/cmsisnn-cifar10, https://github.com/ARM-software/ML-KWS-for-MCU/blob/master/Deployment/Quant_guide.md, or Distiller’s approach (installed with this software).
Further, a quantized network can be refined using post-quantization training (see Distiller).
In all cases, ensure that the quantizer writes out a checkpoint file that the Network Loader can read.
The following step is needed to add new network models:
- Implement a new network model based on the constraints described earlier, see Custom nn.Modules (and
models/ai85net.pyfor an example). The file must include themodelsdata structure that describes the model (name, minimum number of inputs, and whether it can handle 1D or 2D inputs).modelscan list multiple models in the same file.
The following steps are needed for new data formats and datasets:
Develop a data loader in PyTorch, see https://pytorch.org/tutorials/beginner/data_loading_tutorial.html. See datasets/mnist.py for an example.
The data loader must include a loader function, for example mnist_get_datasets(data, load_train=True, load_test=True). data is a tuple of the specified data directory and the program arguments, and the two bools specify whether training and/or test data should be loaded.
The data loader is expected to download and preprocess the datasets as needed and install everything in the specified location.
The loader returns a tuple of two PyTorch Datasets for training and test data.
Add the new data loader to a new file in the datasets directory (for example datasets/mnist.py). The file must include the datasets data structure that describes the dataset and points to the new loader. datasets can list multiple datasets in the same file.
The input key describes the dimensionality of the data, and the first dimension is passed as num_channels to the model, whereas the remaining dimensions are passed as dimension. For example, 'input': (1, 28, 28) will be passed to the model as num_channels=1 and dimensions=(28,28).
The optional regression key in the structure can be set to True to automatically select the --regression command line argument. regression defaults to False.
The optional visualize key can point to a custom visualization function used when creating --embedding. The input to the function (format NCHW for 2D data, or NCL for 1D data) is a batch of data (with N ≤ 100). The default handles square RGB or monochrome images. For any other data, a custom function must be supplied.
The training/verification data is located (by default) in data/DataSetName, for example data/CIFAR10. The location can be overridden with the --data target_directory command line argument.
Train the new network/new dataset. See scripts/train_mnist.sh for a command line example.
The Netron tool (https://github.com/lutzroeder/Netron) can visualize networks, similar to what is available within Tensorboard. To use Netron, use train.py to export the trained network to ONNX, and upload the ONNX file.
(ai8x-training) $ ./train.py --model ai85net5 --dataset MNIST --evaluate --exp-load-weights-from checkpoint.pth.tar --device MAX78000 --summary onnxThe ai8xize network loader currently depends on PyTorch and Nervana’s Distiller. This requirement will be removed in the future.
The network loader creates C code that programs the MAX78000/MAX78002 (for embedded execution, or RTL simulation). Additionally, the generated code contains sample input data and the expected output for the sample, as well as code that verifies the expected output.
The ai8xize.py program needs two inputs:
- A quantized checkpoint file, generated by the MAX78000/MAX78002 model quantization program
quantize.py. - A YAML description of the network.
By default, the C code is split into two files: main.c contains the wrapper code, and loads a sample input and verifies the output for the sample input. cnn.c contains functions that are generated for a specific network to load, configure, and run the accelerator. During development, this split makes it easier to swap out only the network while keeping customized wrapper code intact.
The following table describes the most important command line arguments for ai8xize.py. Use --help for a complete list.
| Argument | Description | Example |
|---|---|---|
--help |
Complete list of arguments | |
| Device selection | ||
--device |
Set device (default: AI84) | --device MAX78000 |
| Hardware features | ||
--avg-pool-rounding |
Round average pooling results | |
--simple1b |
Use simple XOR instead of 1-bit multiplication | |
| Embedded code | ||
--config-file |
YAML configuration file containing layer configuration | --config-file cfg.yaml |
--checkpoint-file |
Checkpoint file containing quantized weights | --checkpoint-file chk.pth.tar |
--display-checkpoint |
Show parsed checkpoint data | |
--prefix |
Set test name prefix | --prefix mnist |
--board-name |
Set the target board (default: EvKit_V1) |
--board-name FTHR_RevA |
| Code generation | ||
--compact-data |
Use memcpy to load input data in order to save code space | |
--compact-weights |
Use memcpy to load weights in order to save code space | |
--mexpress |
Use faster kernel loading | |
--mlator |
Use hardware to swap output bytes (useful for large multi-channel outputs) | |
--softmax |
Add software Softmax functions to generated code | |
--boost |
Turn on a port pin to boost the CNN supply | --boost 2.5 |
--timer |
Insert code to time the inference using a timer | --timer 0 |
| File names | ||
--c-filename |
Main C file name base (default: main.c) | --c-filename main.c |
--api-filename |
API C file name (default: cnn.c) | --api-filename cnn.c |
--weight-filename |
Weight header file name (default: weights.h) | --weight-filename wt.h |
--sample-filename |
Sample data header file name (default: sampledata.h) | --sample-filename kat.h |
--sample-input |
Sample data source file name (default: tests/sample_dataset.npy) | --sample-input kat.npy |
| Streaming and FIFOs | ||
--fifo |
Use FIFOs to load streaming data | |
--fast-fifo |
Use fast FIFO to load streaming data | |
--fast-fifo-quad |
Use fast FIFO in quad fanout mode (implies --fast-fifo) | |
| RISC-V | ||
--riscv |
Use RISC-V processor | |
--riscv-debug |
Use RISC-V processor and enable the RISC-V JTAG | |
--riscv-exclusive |
Use exclusive SRAM access for RISC-V (implies --riscv) | |
| Debug and logging | ||
-v, --verbose |
Verbose output | |
-L, --log |
Redirect stdout to log file | |
--log-intermediate |
Log data between layers | |
--log-pooling |
Log unpooled and pooled data between layers in CSV format | |
--log-filename |
Log file name (default: log.txt) | --log-filename run.log |
-D, --debug |
Debug mode | |
--debug-computation |
Debug computation (SLOW) | |
--stop-after |
Stop after layer | --stop-after 2 |
--one-shot |
Use layer-by-layer one-shot mechanism | |
| Streaming tweaks | ||
--overlap-data |
Allow output to overwrite input | |
--override-start |
Override auto-computed streaming start value (x8 hex) | |
--increase-start |
Add integer to streaming start value (default: 2) | |
--override-rollover |
Override auto-computed streaming rollover value (x8 hex) | |
--override-delta1 |
Override auto-computed streaming delta1 value (x8 hex) | |
--increase-delta1 |
Add integer to streaming delta1 value (default: 0) | |
--override-delta2 |
Override auto-computed streaming delta2 value (x8 hex) | |
--increase-delta2 |
Add integer to streaming delta2 value (default: 0) | |
--ignore-streaming |
Ignore all 'streaming' layer directives | |
| Power saving | ||
--powerdown |
Power down unused MRAM instances | |
--deepsleep |
Put ARM core into deep sleep | |
| Hardware settings | ||
--input-offset |
First layer input offset (x8 hex, defaults to 0x0000) | --input-offset 2000 |
--mlator-noverify |
Do not check both mlator and non-mlator output | |
--write-zero-registers |
Write registers even if the value is zero | |
--init-tram |
Initialize TRAM to 0 | |
--zero-sram |
Zero memories | |
--zero-unused |
Zero unused registers | |
--ready-sel |
Specify memory waitstates | |
--ready-sel-fifo |
Specify FIFO waitstates | |
--ready-sel-aon |
Specify AON waitstates |
An example network description for the ai85net5 architecture and MNIST is shown below:
# CHW (big data) configuration for MNIST
arch: ai85net5
dataset: MNIST
# Define layer parameters in order of the layer sequence
layers:
- pad: 1
activate: ReLU
out_offset: 0x2000
processors: 0x0000000000000001
data_format: CHW
op: conv2d
- max_pool: 2
pool_stride: 2
pad: 2
activate: ReLU
out_offset: 0
processors: 0xfffffffffffffff0
op: conv2d
- max_pool: 2
pool_stride: 2
pad: 1
activate: ReLU
out_offset: 0x2000
processors: 0xfffffffffffffff0
op: conv2d
- avg_pool: 2
pool_stride: 2
pad: 1
activate: ReLU
out_offset: 0
processors: 0x0ffffffffffffff0
op: conv2d
- op: mlp
flatten: true
out_offset: 0x1000
output_width: 32
processors: 0x0000000000000fffTo generate an embedded MAX78000 demo in the demos/ai85-mnist/ folder, use the following command line:
(ai8x-synthesize) $ ./ai8xize.py --verbose --log --test-dir demos --prefix ai85-mnist --checkpoint-file trained/ai85-mnist.pth.tar --config-file networks/mnist-chw-ai85.yaml --device MAX78000 --compact-data --mexpress --softmaxRunning this command will combine the network described above with a fully connected software classification layer. The generated code will include all loading, unloading, and configuration steps.
To generate an RTL simulation for the same network and sample data in the directory tests/ai85-mnist-.... (where .... is an autogenerated string based on the network topology), use:
(ai8x-synthesize) $ ./ai8xize.py --rtl --verbose --autogen rtlsim --log --test-dir rtlsim --prefix ai85-mnist --checkpoint-file trained/ai85-mnist.pth.tar --config-file networks/mnist-chw-ai85.yaml --device MAX78000Network descriptions are written in YAML (see https://en.wikipedia.org/wiki/YAML). There are two sections in each file — global statements and a sequence of layer descriptions.
The network description must match the model that was used for training. In effect, the checkpoint file contains the trained weights, and the YAML file contains a description of the network structure. Additionally, the YAML file (sometimes also called “sidecar file”) describes which processors to use (processors) and which offsets to read and write data from (in_offset and out_offset).
For simple networks with relatively small data dimensions, the easiest way to use the data offsets is by “ping-ponging” between offset 0 and half the memory (offset 0x4000). For example, the input is loaded at offset 0, and the first layer produces its output at offset 0x4000. The second layer reads from 0x4000 and writes to 0. Assuming the dimensions are small enough, this easy method avoids overwriting an input that has not yet been consumed by the accelerator.
arch specifies the network architecture, for example ai84net5. This key is matched against the architecture embedded in the checkpoint file.
The bias configuration is only used for test data. To use bias with trained networks, use the bias parameter in PyTorch’s nn.Module.Conv2d() function. The converter tool will then automatically add bias parameters as needed.
dataset configures the data set for the network. This determines the input data size and dimensions as well as the number of input channels.
Data sets are for example mnist, fashionmnist, and cifar-10.
The global output_map, if specified, overrides the memory instances where the last layer outputs its results. If not specified, this will be either the output_processors specified for the last layer, or, if that key does not exist, default to the number of processors needed for the output channels, starting at 0.
Example:
output_map: 0x0000000000000ff0
layers is a list that defines the per-layer description.
Each layer in the layers list describes the layer’s processors, convolution type, activation, pooling, weight and output sizes, data input format, data memory offsets, and its processing sequence. Several examples are located in the networks/ and tests/ folders.
This key allows overriding of the processing sequence. The default is 0 for the first layer, or the previous layer’s sequence + 1 for other layers.
sequence numbers may have gaps. The software will sort layers by their numeric value, with the lowest value first.
processors specifies which processors will handle the input data. The processor map must match the number of input channels, and the input data format. For example, in CHW format, processors must be attached to different data memory instances.
processors is specified as a 64-bit hexadecimal value. Dots (‘.’) and a leading ‘0x’ are ignored.
Note: When using multi-pass (i.e., using more than 64 channels), the number processors is an integer division of the channel count, rounded up. For example, 60 processors are specified for 120 channels.
Example for three processors 0, 4, and 8:
processors: 0x0000.0000.0000.0111
Example for four processors 0, 1, 2, and 3:
processors: 0x0000.0000.0000.000f
output_processors specifies which data memory instances and 32-bit word offsets to use for the layer’s output data. When not specified, this key defaults to the next layer’s processors, or, for the last layer, to the global output_map. output_processors is specified as a 64-bit hexadecimal value. Dots (‘.’) and a leading ‘0x’ are ignored.
out_offset specifies the relative offset inside the data memory instance where the output data should be written to. When not specified, out_offset defaults to 0. See also [Data Memory Ping-Pong](#Data Memory Ping-Pong).
Example:
out_offset: 0x2000
in_offset specifies the offset into the data memory instances where the input data should be loaded from. When not specified, this key defaults to the previous layer’s out_offset, or 0 for the first layer.
Example:
in_offset: 0x2000
When not using an activation, the last layer can output 32 bits of unclipped data in Q17.14 format. The default is 8 bits. Note that the corresponding model’s last layer must be trained with wide=True when output_width is 32.
Example:
output_width: 32
When specified for the first layer only, data_format can be either chw/big or hwc/little. The default is hwc. Note that the data format interacts with processors, see [Channel Data Formats](#Channel Data Formats).
This key (which can also be specified using op, operator, or convolution) selects a layer’s main operation after the optional input pooling.
When this key is not specified, a warning is displayed and Conv2d is selected.
| Operation | Description |
|---|---|
Conv1d |
1D convolution over an input composed of several input planes |
Conv2d |
2D convolution over an input composed of several input planes |
ConvTranspose2d |
2D transposed convolution (upsampling) over an input composed of several input planes |
None or Passthrough |
No operation (note: input and output processors must be the same) |
Linear or FC or MLP |
Linear transformation to the incoming data |
Add |
Element-wise addition |
Sub |
Element-wise subtraction |
Xor |
Element-wise binary XOR |
Or |
Element-wise binary OR |
Element-wise operations default to two operands. This can be changed using the operands key.
Element-wise operations can also be added “in-flight” to Conv2d. In this case, the element-wise operation is specified using the eltwise key.
Note: On MAX78000, this is only supported for 64 channels, or up to 128 channels when only two operands are used. Use a separate layer for the element-wise operation when more operands or channels are needed instead of combining the element-wise operator with a convolution.
Example:
eltwise: add
When using both pooling and element-wise operations, pooling is performed first by default. Optionally, the element-wise operation can be performed before the pooling operation by setting pool_first to False.
Example:
pool_first: false
For any element-wise operation, this key configures the number of operands from 2 to 16 inclusive. The default is 2.
Example:
operation: add
operands: 4
This key describes whether to activate the layer output (the default is to not activate). When specified, this key must be ReLU, Abs or None (the default).
Note that the output values are clipped (saturated) to ReLU behaves more similar to PyTorch’s nn.Hardtanh(min_value=0, max_value=127) than to PyTorch’s nn.ReLU().
Note that output_shift can be used for (limited) “linear” activation.
This key describes the width of the weight memory in bits and can be 1, 2, 4, or 8 (the default is based on the range of the layer’s weights). Specifying a quantization that is smaller than what the weights require results in an error message. By default, the value is automatically derived from the weights.
Example:
quantization: 4
When output_width is 8, the 32-bit intermediate result can be shifted left or right before reduction to 8-bit. The value specified here is cumulative with the value generated from and used by quantization. Note that output_shift is not supported for passthrough layers.
The 32-bit intermediate result is multiplied by
| weight quantization | shift used by quantization | available range for output_shift
|
|---|---|---|
| 8-bit | 0 | |
| 4-bit | 4 | |
| 2-bit | 6 | |
| 1-bit | 7 |
Using output_shift can help normalize data, particularly when using small weights. By default, output_shift is generated by the training software, and it is used for batch normalization as well as quantization-aware training.
Example:
output_shift: 2
2D convolutions:
This key must be 3x3 (the default) or 1x1.
1D convolutions:
This key must be 1 through 9.
Example:
kernel_size: 1x1
This key must be 1 or [1, 1].
pad sets the padding for the convolution.
- For
Conv2d, this value can be0,1(the default), or2. - For
Conv1d, the value can be0,1,2, or3(the default). - For
Passthrough, this value must be0(the default).
When specified, performs a MaxPool before the convolution. The pooling size can specified as an integer (when the value is identical for both dimensions, or for 1D convolutions), or as two values in order [H, W].
Example:
max_pool: 2
When specified, performs an AvgPool before the convolution. The pooling size can specified as an integer (when the value is identical for both dimensions, or for 1D convolutions), or as two values in order [H, W].
Example:
avg_pool: 2
When performing a pooling operation, this key describes the pool stride. The pooling stride can be specified as an integer (when the value is identical for both dimensions, or for 1D convolutions), or as two values in order [H, W], where both values must be identical. The default is 1 or [1, 1].
Example:
pool_stride: [2, 2]
in_channels specifies the number of channels of the input data. This is usually automatically computed based on the weights file and the layer sequence. This key allows overriding of the number of channels. See also: in_dim.
Example:
in_channels: 8
in_dim specifies the dimensions of the input data. This is usually automatically computed based on the output of the previous layer or the layer(s) referenced by in_sequences. This key allows overriding of the automatically calculated dimensions. in_dim must be used when changing from 1D to 2D data or vice versa.
See also: in_channels.
Example:
in_dim: [64, 64]
By default, a layer’s input is the output of the previous layer. in_sequences can be used to point to the output of one or more arbitrary previous layers, for example when processing the same data using two different kernel sizes, or when combining the outputs of several prior layers. in_sequences can be specified as an integer (for a single input) or as a list (for multiple inputs). As a special case, -1 is the input data. The in_offset and out_offset must be set to match the specified sequence.
Example:
in_sequences: [2, 3]
See the Fire example for a network that uses in_sequences.
out_channels specifies the number of channels of the output data. This is usually automatically computed based on the weights and layer sequence. This key allows overriding the number of output channels.
Example:
out_channels: 8
streaming specifies that the layer is using streaming mode. this is necessary when the input data dimensions exceed the available data memory. When enabling streaming, all prior layers have to enable streaming as well. streaming can be enabled for up to 8 layers.
Example:
streaming: true
flatten specifies that 2D input data should be transformed to 1D data for use by a Linear layer. Note that flattening cannot be used in the same layer as pooling.
Example:
flatten: true
write_gap specifies the number of channels that should be skipped during write operations (this value is multiplied with the output multi-pass, i.e., write every nth word where n = write_gap × output_multipass). This creates an interleaved output that can be used as the input for subsequent layers that use an element-wise operation, or to concatenate multiple inputs to form data with more than 64 channels.
Set write_gap to 1 to produce output for a subsequent two-input element-wise operation.
Example:
write_gap: 1
The following shows an example for a single “Fire” operation, the MAX78000/MAX78002 hardware layer numbers and its YAML description.
arch: ai85firetestnet
dataset: CIFAR-10
# Input dimensions are 3x32x32
layers:
### Fire
# Squeeze
- avg_pool: 2
pool_stride: 2
pad: 0
in_offset: 0x1000
processors: 0x0000000000000007
data_format: HWC
out_offset: 0x0000
operation: conv2d
kernel_size: 1x1
activate: ReLU
# Expand 1x1
- in_offset: 0x0000
out_offset: 0x1000
processors: 0x0000000000000030
output_processors: 0x0000000000000f00
operation: conv2d
kernel_size: 1x1
pad: 0
activate: ReLU
# Expand 3x3
- in_offset: 0x0000
out_offset: 0x1000
processors: 0x0000000000000030
output_processors: 0x000000000000f000
operation: conv2d
kernel_size: 3x3
activate: ReLU
in_sequences: 0
# Concatenate
- max_pool: 2
pool_stride: 2
in_offset: 0x1000
out_offset: 0x0000
processors: 0x000000000000ff00
operation: none
in_sequences: [1, 2]
### Additional layers
- max_pool: 2
pool_stride: 2
out_offset: 0x1000
processors: 0x000000000000ff00
operation: none
- flatten: true
out_offset: 0x0000
op: mlp
processors: 0x000000000000ff00
output_width: 32Many networks use residual connections. In the following example, the convolution on the right works on the output data of the first convolution. However, that same output data also “bypasses” the second convolution and is added to the output.
On MAX78000/MAX78002, the element-wise addition works on “interleaved data”, i.e., each machine fetch gathers one operand.
In order to achieve this, a layer must be inserted that does nothing else but reformat the data into interleaved format using the write_gap keyword (this operation happens in parallel and is fast).
# Layer 1
- out_offset: 0x0000
processors: 0x0ffff00000000000
operation: conv2d
kernel_size: 3x3
pad: 1
activate: ReLU
# Layer 2 - re-format data with gap
- out_offset: 0x2000
processors: 0x00000000000fffff
output_processors: 0x00000000000fffff
operation: passthrough
write_gap: 1
# Layer 3
- in_offset: 0x0000
out_offset: 0x2004
processors: 0x00000000000fffff
operation: conv2d
kernel_size: 3x3
pad: 1
activate: ReLU
write_gap: 1
# Layer 4 - Residual
- in_sequences: [2, 3]
in_offset: 0x2000
out_offset: 0x0000
processors: 0x00000000000fffff
eltwise: add
...The same network can also be viewed graphically:
Adding new datasets to the Network Loader is implemented as follows:
-
Provide the network model, its YAML description and weights. Place the YAML file (e.g.,
new.yaml) in thenetworksdirectory, and weights in thetraineddirectory. The non-quantized weights are obtained from a training checkpoint, for example:(ai8x-synthesis) $ cp ../ai8x-training/logs/2020.06.02-154133/best.pth.tar trained/new-unquantized.pth.tar -
When using post-training quantization, the quantized weights are the result of the quantization step. Copy and customize an existing quantization shell script, for example:
(ai8x-synthesis) $ cp scripts/quantize_mnist.sh scripts/quantize_new.shThen, edit this script to point to the new model and dataset (
vi scripts/quantize_new.sh), and call the script to generate the quantized weights. Example:(ai8x-synthesis) $ scripts/quantize_new.sh Configuring device: MAX78000. Reading networks/new.yaml to configure network... Converting checkpoint file trained/new-unquantized.pth.tar to trained/new.pth.tar +----------------------+-------------+----------+ | Key | Type | Value | |----------------------+-------------+----------| | arch | str | ai85net5 | | compression_sched | dict | | | epoch | int | 165 | | extras | dict | | | optimizer_state_dict | dict | | | optimizer_type | type | SGD | | state_dict | OrderedDict | | +----------------------+-------------+----------+ Model keys (state_dict): conv1.conv2d.weight, conv2.conv2d.weight, conv3.conv2d.weight, conv4.conv2d.weight, fc.linear.weight, fc.linear.bias conv1.conv2d.weight avg_max: tensor(0.3100) max: tensor(0.7568) mean: tensor(0.0104) factor: 54.4 bits: 8 conv2.conv2d.weight avg_max: tensor(0.1843) max: tensor(0.2897) mean: tensor(-0.0063) factor: 108.8 bits: 8 conv3.conv2d.weight avg_max: tensor(0.1972) max: tensor(0.3065) mean: tensor(-0.0053) factor: 108.8 bits: 8 conv4.conv2d.weight avg_max: tensor(0.3880) max: tensor(0.5299) mean: tensor(0.0036) factor: 108.8 bits: 8 fc.linear.weight avg_max: tensor(0.6916) max: tensor(0.9419) mean: tensor(-0.0554) factor: 108.8 bits: 8 fc.linear.bias
-
Provide a sample input. The sample input is used to generate a known-answer test (self test). The sample input is provided as a NumPy “pickle” — add
sample_dset.npyfor the dataset nameddsetto thetestsdirectory. This file can be generated by saving a sample in CHW format (no batch dimension) usingnumpy.save(), see below.For example, the MNIST 1×28×28 image sample would be stored in
tests/sample_mnist.npyin annp.arraywith shape[1, 28, 28]and datatype<i8. The file can be random, or can be obtained from thetrain.pysoftware.
To generate a random sample input, use a short NumPy script. In the MNIST example:
import os
import numpy as np
a = np.random.randint(-128, 127, size=(1, 28, 28), dtype=np.int64)
np.save(os.path.join('tests', 'sample_mnist'), a, allow_pickle=False, fix_imports=False)-
In the
ai8x-trainingproject, add the argument--save-sample 10to thescripts/evaluate_mnist.shscript. Note: The index 10 is arbitrary, but it must be smaller than the batch size. If manual visual verification is desired, it is a good idea to pick a sample where the quantized model computes the correct answer. -
Run the modified
scripts/evaluate_mnist.sh. It will produce a file namedsample_mnist.npy. -
Save the
sample_mnist.npyfile and copy it to theai8x-synthesisproject.
- Switch to training project directory and activate the environment:
(ai8x-synthesis) $ deactivate` $ cd ../ai8x-training $ source bin/activate
- Create an evaluation script and run it:
Example output:
(ai8x-training) $ cp scripts/evaluate_mnist.sh scripts/evaluate_new.sh (ai8x-training) $ vim scripts/evaluate_new.sh (ai8x-training) $ scripts/evaluate_new.sh
(ai8x-training) $ scripts/evaluate_new.sh Configuring device: MAX78000, simulate=True. Log file for this run: logs/2020.06.03-125328/2020.06.03-125328.log -------------------------------------------------------- Logging to TensorBoard - remember to execute the server: > tensorboard --logdir='./logs' => loading checkpoint ../ai8x-synthesis/trained/new.pth.tar => Checkpoint contents: +----------------------+-------------+----------+ | Key | Type | Value | |----------------------+-------------+----------| | arch | str | ai85net5 | | compression_sched | dict | | | epoch | int | 165 | | extras | dict | | | optimizer_state_dict | dict | | | optimizer_type | type | SGD | | state_dict | OrderedDict | | +----------------------+-------------+----------+ => Checkpoint['extras'] contents: +-----------------+--------+-------------------+ | Key | Type | Value | |-----------------+--------+-------------------| | best_epoch | int | 165 | | best_top1 | float | 99.46666666666667 | | clipping_method | str | SCALE | | clipping_scale | float | 0.85 | | current_top1 | float | 99.46666666666667 | +-----------------+--------+-------------------+ Loaded compression schedule from checkpoint (epoch 165) => loaded 'state_dict' from checkpoint '../ai8x-synthesis/trained/new.pth.tar' Optimizer Type: <class 'torch.optim.sgd.SGD'> Optimizer Args: {'lr': 0.1, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0.0001, 'nesterov': False} Dataset sizes: training=54000 validation=6000 test=10000 --- test --------------------- 10000 samples (256 per mini-batch) Test: [ 10/ 40] Loss 44.193750 Top1 99.609375 Top5 99.960938 Test: [ 20/ 40] Loss 66.567578 Top1 99.433594 Top5 99.980469 Test: [ 30/ 40] Loss 51.816276 Top1 99.518229 Top5 99.986979 Test: [ 40/ 40] Loss 53.596094 Top1 99.500000 Top5 99.990000 ==> Top1: 99.500 Top5: 99.990 Loss: 53.596 ==> Confusion: [[ 979 0 0 0 0 0 0 0 1 0] [ 0 1132 1 0 0 0 0 2 0 0] [ 2 0 1026 1 0 0 0 3 0 0] [ 0 0 0 1009 0 0 0 0 1 0] [ 0 0 0 0 978 0 0 0 0 4] [ 1 0 0 3 0 886 1 0 0 1] [ 5 4 1 0 1 0 946 0 1 0] [ 0 1 3 0 0 0 0 1023 0 1] [ 0 0 0 1 1 1 0 0 970 1] [ 0 0 0 0 4 1 0 3 0 1001]] Log file for this run: logs/2020.06.03-125328/2020.06.03-125328.log
Run ai8xize.py with the new network and the new sample data to generate embedded C code that can be compiled with the Arm and RISC-V compilers. See gen-demos-max78000.sh for examples.
An inference is started by configuring registers and weights, loading the input, and enabling processing. This code is automatically generated—see the cnn_init(), cnn_load_weights(), cnn_load_bias(), cnn_configure(), and load_input() functions. The sample data can be used as a self-checking feature on device power-up since the output for the sample data is known.
To start the accelerator, use cnn_start(). The load_input() function is called either before cnn_start(), or after cnn_start(), depending on whether FIFOs are used. To run a second inference with new data, call cnn_start() again (after or before loading the new data input using load_input()`).
The MAX78000/MAX78002 accelerator can generate an interrupt on completion, and it will set a status bit (see cnn.c). The resulting data can now be unloaded from the accelerator (code for this is also auto-generated in cnn_unload()).
To run another inference, ensure all groups are disabled (stopping the state machine, as shown in cnn_init()). Next, load the new input data and start processing.
ai8xize.py can generate a call to a software Softmax function using the command line switch --softmax. That function is provided in the assets/device-all folder. To use the provided software Softmax on MAX78000/MAX78002, the last layer output should be 32-bit wide (output_width: 32).
The software Softmax function is optimized for processing time and it quantizes the input.
The API code (in cnn.c by default) is auto-generated. It is data independent, but differs depending on the network. This simplifies replacing the network while keeping the remainder of the code intact.
The functions that do not return void return either CNN_FAIL or CNN_OK as specified in the auto-generated cnn.h header file. The header file also contains a definition for the number of outputs of the network (CNN_NUM_OUTPUTS). In limited circumstances, this can make the wrapper code somewhat network independent.
int cnn_enable(uint32_t clock_source, uint32_t clock_divider);
Enable clocks (from clock_source with clock_divider) and power to the accelerator; also enable the accelerator interrupt. By default, on MAX78000, the accelerator runs at 50 MHz (APB clock or PCLK divided by 1).
int cnn_disable(void);
Disable clocks and power to the accelerator.
int cnn_init(void);
Perform minimum accelerator initialization so it can be configured or restarted.
int cnn_configure(void);
Configure the accelerator for the given network.
int cnn_load_weights(void);
Load the accelerator weights.
int cnn_verify_weights(void);
Verify the accelerator weights (used for debug only).
int cnn_load_bias(void);
Load accelerator the bias values (if needed).
int cnn_start(void);
Start accelerator processing.
int cnn_stop(void);
Force-stop the accelerator regardless of whether it has finished or not.
int cnn_continue(void);
Continue accelerator processing after force-stopping it.
int cnn_unload(uint32_t *out_buf);
Unload the results from the accelerator. The output buffer must be 32-bit aligned (round up to the next 32-bit size when using 8-bit outputs).
int cnn_boost_enable(mxc_gpio_regs_t *port, uint32_t pin);
Turn on the boost circuit on port.pin. This is only needed for very energy intense networks. Use the --boost command line option to insert calls to this function in the wrapper code.
int cnn_boost_disable(mxc_gpio_regs_t *port, uint32_t pin);
Turn off the boost circuit connected to port.pin.
- For MAX78000/MAX78002, the software Softmax is implemented in
softmax.c. - A template for the
cnn.hheader file intemplatecnn.h. The template is customized during code generation.
The MAX78000 Evaluation Kit (EVKit) revision C and later includes a MAX32625 microcontroller connected to a MAX34417 power accumulator. Since the sample rate of the MAX34417 is slow compared to typical inference times, ai8xize.py supports the command line parameter --energy that will run 100 iterations of the inference, separating out the input data load time. This allows enough sample time to get meaningful results (recommended minimum: 1 second).
When running C code generated with --energy, the power display on the EVKit will display the inference energy.
Note: MAX78000 uses LED1 and LED2 to trigger power measurement via MAX32625 and MAX34417.
See https://github.com/MaximIntegratedAI/MaximAI_Documentation/blob/master/MAX78000_Evaluation_Kit/MAX78000%20Power%20Monitor%20and%20Energy%20Benchmarking%20Guide.pdf for more information about benchmarking.
Additional information about the evaluation kits, and the software development kit (SDK) is available on the web at https://github.com/MaximIntegratedAI/aximAI_Documentation
The following tables show the AHB memory addresses for the MAX78000 accelerator:
Total: 512 KiB (16 instances of 8192 × 32)
| Group | Instance | Address Range |
|---|---|---|
| 0 | 0 | 0x50400000 - 0x50407FFF |
| 0 | 1 | 0x50408000 - 0x5040FFFF |
| 0 | 2 | 0x50410000 - 0x50417FFF |
| 0 | 3 | 0x50418000 - 0x5041FFFF |
| 1 | 0 | 0x50800000 - 0x50807FFF |
| 1 | 1 | 0x50808000 - 0x5080FFFF |
| 1 | 2 | 0x50810000 - 0x50817FFF |
| 1 | 3 | 0x50818000 - 0x5081FFFF |
| 2 | 0 | 0x50C00000 - 0x50C07FFF |
| 2 | 1 | 0x50C08000 - 0x50C0FFFF |
| 2 | 2 | 0x50C10000 - 0x50C17FFF |
| 2 | 3 | 0x50C18000 - 0x50C1FFFF |
| 3 | 0 | 0x51000000 - 0x51007FFF |
| 3 | 1 | 0x51008000 - 0x5100FFFF |
| 3 | 2 | 0x51010000 - 0x51017FFF |
| 3 | 3 | 0x51018000 - 0x5101FFFF |
Total: 384 KiB (64 instances of 3072 × 16)
| Group | Instance | Address Range* |
|---|---|---|
| 0 | 0 | 0x50110000 - 0x50112FFF |
| 0 | 1 | 0x50114000 - 0x50116FFF |
| 0 | 2 | 0x50118000 - 0x5011AFFF |
| 0 | 3 | 0x5011C000 - 0x5011EFFF |
| 0 | 4 | 0x50120000 - 0x50122FFF |
| 0 | 5 | 0x50124000 - 0x50126FFF |
| 0 | 6 | 0x50128000 - 0x5012AFFF |
| 0 | 7 | 0x5012C000 - 0x5012EFFF |
| 0 | 8 | 0x50130000 - 0x50132FFF |
| 0 | 9 | 0x50134000 - 0x50136FFF |
| 0 | 10 | 0x50138000 - 0x5013AFFF |
| 0 | 11 | 0x5013C000 - 0x5013EFFF |
| 0 | 12 | 0x50140000 - 0x50142FFF |
| 0 | 13 | 0x50144000 - 0x50146FFF |
| 0 | 14 | 0x50148000 - 0x5014AFFF |
| 0 | 15 | 0x5014C000 - 0x5014EFFF |
| 1 | 0 | 0x50510000 - 0x50512FFF |
| 1 | 1 | 0x50514000 - 0x50516FFF |
| 1 | 2 | 0x50518000 - 0x5051AFFF |
| 1 | 3 | 0x5051C000 - 0x5051EFFF |
| 1 | 4 | 0x50520000 - 0x50522FFF |
| 1 | 5 | 0x50524000 - 0x50526FFF |
| 1 | 6 | 0x50528000 - 0x5052AFFF |
| 1 | 7 | 0x5052C000 - 0x5052EFFF |
| 1 | 8 | 0x50530000 - 0x50532FFF |
| 1 | 9 | 0x50534000 - 0x50536FFF |
| 1 | 10 | 0x50538000 - 0x5053AFFF |
| 1 | 11 | 0x5053C000 - 0x5053EFFF |
| 1 | 12 | 0x50540000 - 0x50542FFF |
| 1 | 13 | 0x50544000 - 0x50546FFF |
| 1 | 14 | 0x50548000 - 0x5054AFFF |
| 1 | 15 | 0x5054C000 - 0x5054EFFF |
| 2 | 0 | 0x50910000 - 0x50912FFF |
| 2 | 1 | 0x50914000 - 0x50916FFF |
| 2 | 2 | 0x50918000 - 0x5091AFFF |
| 2 | 3 | 0x5091C000 - 0x5091EFFF |
| 2 | 4 | 0x50920000 - 0x50922FFF |
| 2 | 5 | 0x50924000 - 0x50926FFF |
| 2 | 6 | 0x50928000 - 0x5092AFFF |
| 2 | 7 | 0x5092C000 - 0x5092EFFF |
| 2 | 8 | 0x50930000 - 0x50932FFF |
| 2 | 9 | 0x50934000 - 0x50936FFF |
| 2 | 10 | 0x50938000 - 0x5093AFFF |
| 2 | 11 | 0x5093C000 - 0x5093EFFF |
| 2 | 12 | 0x50940000 - 0x50942FFF |
| 2 | 13 | 0x50944000 - 0x50946FFF |
| 2 | 14 | 0x50948000 - 0x5094AFFF |
| 2 | 15 | 0x5094C000 - 0x5094EFFF |
| 3 | 0 | 0x50D10000 - 0x50D12FFF |
| 3 | 1 | 0x50D14000 - 0x50D16FFF |
| 3 | 2 | 0x50D18000 - 0x50D1AFFF |
| 3 | 3 | 0x50D1C000 - 0x50D1EFFF |
| 3 | 4 | 0x50D20000 - 0x50D22FFF |
| 3 | 5 | 0x50D24000 - 0x50D26FFF |
| 3 | 6 | 0x50D28000 - 0x50D2AFFF |
| 3 | 7 | 0x50D2C000 - 0x50D2EFFF |
| 3 | 8 | 0x50D30000 - 0x50D32FFF |
| 3 | 9 | 0x50D34000 - 0x50D36FFF |
| 3 | 10 | 0x50D38000 - 0x50D3AFFF |
| 3 | 11 | 0x50D3C000 - 0x50D3EFFF |
| 3 | 12 | 0x50D40000 - 0x50D42FFF |
| 3 | 13 | 0x50D44000 - 0x50D46FFF |
| 3 | 14 | 0x50D48000 - 0x50D4AFFF |
| 3 | 15 | 0x50D4C000 - 0x50D4EFFF |
*using 32 bits of address space for each 16-bit memory
Total: 432 KiB (64 instances of 768 × 72)
| Group | Instance | Address Range* |
|---|---|---|
| 0 | 0 | 0x50180000 - 0x50182FFF |
| 0 | 1 | 0x50184000 - 0x50186FFF |
| 0 | 2 | 0x50188000 - 0x5018AFFF |
| 0 | 3 | 0x5018c000 - 0x5018DFFF |
| 0 | 4 | 0x50190000 - 0x50191FFF |
| 0 | 5 | 0x50194000 - 0x50196FFF |
| 0 | 6 | 0x50198000 - 0x5019AFFF |
| 0 | 7 | 0x5019C000 - 0x5019DFFF |
| 0 | 8 | 0x501A0000 - 0x501A2FFF |
| 0 | 9 | 0x501A4000 - 0x501A6FFF |
| 0 | 10 | 0x501A8000 - 0x501AAFFF |
| 0 | 11 | 0x501AC000 - 0x501ADFFF |
| 0 | 12 | 0x501B0000 - 0x501B2FFF |
| 0 | 13 | 0x501B4000 - 0x501B6FFF |
| 0 | 14 | 0x501B8000 - 0x501BAFFF |
| 0 | 15 | 0x501BC000 - 0x501BDFFF |
| 1 | 0 | 0x50580000 - 0x50582FFF |
| 1 | 1 | 0x50584000 - 0x50586FFF |
| 1 | 2 | 0x50588000 - 0x5058AFFF |
| 1 | 3 | 0x5058C000 - 0x5058DFFF |
| 1 | 4 | 0x50590000 - 0x50591FFF |
| 1 | 5 | 0x50594000 - 0x50596FFF |
| 1 | 6 | 0x50598000 - 0x5059AFFF |
| 1 | 7 | 0x5059C000 - 0x5059DFFF |
| 1 | 8 | 0x505A0000 - 0x505A2FFF |
| 1 | 9 | 0x505A4000 - 0x505A6FFF |
| 1 | 10 | 0x505A8000 - 0x505AAFFF |
| 1 | 11 | 0x505AC000 - 0x505ADFFF |
| 1 | 12 | 0x505B0000 - 0x505B2FFF |
| 1 | 13 | 0x505B4000 - 0x505B6FFF |
| 1 | 14 | 0x505B8000 - 0x505BAFFF |
| 1 | 15 | 0x505BC000 - 0x505BDFFF |
| 2 | 0 | 0x50980000 - 0x50982FFF |
| 2 | 1 | 0x50984000 - 0x50986FFF |
| 2 | 2 | 0x50988000 - 0x5098AFFF |
| 2 | 3 | 0x5098C000 - 0x5098DFFF |
| 2 | 4 | 0x50990000 - 0x50991FFF |
| 2 | 5 | 0x50994000 - 0x50996FFF |
| 2 | 6 | 0x50998000 - 0x5099AFFF |
| 2 | 7 | 0x5099C000 - 0x5099DFFF |
| 2 | 8 | 0x509A0000 - 0x509A2FFF |
| 2 | 9 | 0x509A4000 - 0x509A6FFF |
| 2 | 10 | 0x509A8000 - 0x509AAFFF |
| 2 | 11 | 0x509AC000 - 0x509ADFFF |
| 2 | 12 | 0x509B0000 - 0x509B2FFF |
| 2 | 13 | 0x509B4000 - 0x509B6FFF |
| 2 | 14 | 0x509B8000 - 0x509BAFFF |
| 2 | 15 | 0x509BC000 - 0x509BDFFF |
| 3 | 0 | 0x50D80000 - 0x50D82FFF |
| 3 | 1 | 0x50D84000 - 0x50D86FFF |
| 3 | 2 | 0x50D88000 - 0x50D8AFFF |
| 3 | 3 | 0x50D8C000 - 0x50D8DFFF |
| 3 | 4 | 0x50D90000 - 0x50D91FFF |
| 3 | 5 | 0x50D94000 - 0x50D96FFF |
| 3 | 6 | 0x50D98000 - 0x50D9AFFF |
| 3 | 7 | 0x50D9C000 - 0x50D9DFFF |
| 3 | 8 | 0x50DA0000 - 0x50DA2FFF |
| 3 | 9 | 0x50DA4000 - 0x50DA6FFF |
| 3 | 10 | 0x50DA8000 - 0x50DAAFFF |
| 3 | 11 | 0x50DAC000 - 0x50DADFFF |
| 3 | 12 | 0x50DB0000 - 0x50DB2FFF |
| 3 | 13 | 0x50DB4000 - 0x50DB6FFF |
| 3 | 14 | 0x50DB8000 - 0x50DBAFFF |
| 3 | 15 | 0x50DBC000 - 0x50DBDFFF |
*using 128 bits of address space for each 72-bit memory
Total: 2 KiB (4 instances of 128 × 32)
| Group | Address Range |
|---|---|
| 0 | 0x50108000 - 0x50109FFF |
| 1 | 0x50508000 - 0x50509FFF |
| 2 | 0x50908000 - 0x50909FFF |
| 3 | 0x50D08000 - 0x50D09FFF |
Both projects are set up for flake8, pylint and isort to lint Python code. The line width is related to 100 (instead of the default of 80), and the number of lines per module was increased; configuration files are included in the projects. Shell code is linted by shellcheck, and YAML files by yamllint.
Code should not generate any warnings in any of the tools (some of the components in the ai8x-training project will create warnings as they are based on third-party code).
flake8 and pylint need to be installed into both virtual environments:
(ai8x-synthesis) $ pip3 install flake8 pylint mypy isortThe GitHub projects use the GitHub Super-Linter to automatically verify push operations and pull requests. The Super-Linter can be installed locally, see installation instructions.
To run locally, create a clean copy of the repository and run the following command from the project directory (i.e., ai8x-training or ai8x-synthesis):
$ docker pull github/super-linter:latest
$ docker run --rm -e RUN_LOCAL=true -e VALIDATE_MARKDOWN=false -e VALIDATE_PYTHON_BLACK=false -e VALIDATE_ANSIBLE=false -e VALIDATE_EDITORCONFIG=false -e FILTER_REGEX_EXCLUDE="attic/.*|inspect_ckpt.py" -v `pwd`:/tmp/lint github/super-linterDo not try to push any changes into the master branch. Instead, create a fork and submit a pull request against the develop branch. The easiest way to do this is using a graphical client such as Fork or GitHub Desktop.
Note: After creating the fork, you must re-enable actions in the “Actions” tab of the repository on GitHub.
The following document has more information: https://github.com/MaximIntegratedAI/MaximAI_Documentation/blob/master/CONTRIBUTING.md