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Benchmark
Lianmin Zheng edited this page Aug 1, 2018
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Note: If a board has big.LITTLE architecture, we will use all big cores. Otherwise, we will use all cores. In the following device specifications, we only list the cores being used. We use dtype=float32 and batch_size=1 for the following benchmark.
- Firefly-RK3399 : 2 x Cortex A73 1.8Ghz
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Network Name Mean Inference Time (std dev)
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squeezenet v1.1 48.87 ms (1.07 ms)
mobilenet 82.16 ms (0.09 ms)
resnet-18 162.55 ms (0.14 ms)
vgg-16 912.44 ms (0.32 ms)
- Raspberry Pi 3B : 4 x Cortex A53 1.2Ghz
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Network Name Mean Inference Time (std dev)
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squeezenet v1.1 92.34 ms (0.07 ms)
mobilenet 145.22 ms (0.11 ms)
resnet-18 325.06 ms (0.23 ms)
vgg-16 crashed due to out of memeory
- Huawei P20 Pro / Mate10 Pro (Soc: HiSilicon Kirin 970) : (4 x Cortex A73 2.36GHz)
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Network Name Mean Inference Time (std dev)
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squeezenet v1.1 27.53 ms (1.14 ms)
mobilenet 46.53 ms (0.31 ms)
resnet-18 76.74 ms (0.18 ms)
vgg-16 479.84 ms (0.92 ms)
- Google Pixel 2 (Soc: Qualcomm Snapdragon 835) : (4 × Kyro 2.35 GHz)
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Network Name Mean Inference Time (std dev)
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squeezenet v1.1 23.57 ms (0.42 ms)
mobilenet 40.73 ms (0.11 ms)
resnet-18 63.95 ms (0.03 ms)
vgg-16 407.75 ms (9.57 ms)
- PYNQ (2 x Cortex-A9 650MHz)
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Network Name Mean Inference Time (std dev)
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squeezenet v1.1 452.40 ms (0.09 ms)
mobilenet 772.16 ms (0.25 ms)
resnet-18 1243.49 ms (0.67 ms)
vgg-16 crashed due to out of memeory
see readme page here https://github.com/dmlc/tvm/tree/master/apps/benchmark on how to get these numbers