TinyTTA Engine is a lightweight framework for enabling Test-Time Adaptation (TTA) on edge devices like microcontrollers (MCUs). Built upon TFLM, it features:
- On-device backpropagation with floating-point computation
- Support for common DNN operators (ReLU, FullyConnected, Softmax, Maxpool, Avgpool, Conv, DepthwiseConv)
- Memory-efficient layer-wise update strategy with dynamic allocation
- Automatic differentiation for backward graph construction
- Graph optimization through operation fusion and quantization
The framework is specifically designed to operate within the resource constraints of edge devices while enabling model adaptation capabilities.
Note: Fully public release of the code is coming soon! Stay tuned for updates.
If you have any questions please email at [email protected] and [email protected]
If you find this code useful for your research, please consider citing the following papers:
@article{jia2024tinytta,
title={TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices},
author={Jia, Hong and Kwon, Young and Orsino, Alessio and Dang, Ting and Talia, Domenico and Mascolo, Cecilia},
journal={Advances in Neural Information Processing Systems},
volume={37},
pages={43274--43299},
year={2024}
}