On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. Users can benefit from customized AI models without having to transfer the data to the cloud, protecting the privacy. However, the training memory consumption is prohibitive for IoT devices that have tiny memory resources. We propose an algorithm-system co-design framework to make on-device training possible with only 256KB of memory. On-device training faces two unique challenges: (1) the quantized graphs of neural networks are hard to optimize due to low bit-precision and the lack of normalization; (2) the limited hardware resource does not allow full back-propagation. To cope with the optimization difficulty, we propose Quantization-Aware Scaling to calibrate the gradient scales and stabilize 8-bit quantized training. To reduce the memory footprint, we propose Sparse Update to skip the gradient computation of less important layers and sub-tensors. The algorithm innovation is implemented by a lightweight training system, Tiny Training Engine, which prunes the backward computation graph to support sparse updates and offload the runtime auto-differentiation to compile time. Our framework is the first solution to enable tiny on-device training of convolutional neural networks under 256KB SRAM and 1MB Flash without auxiliary memory, using less than 1/1000 of the memory of PyTorch and TensorFlow while matching the accuracy on tinyML application VWW. Our study enables IoT devices not only to perform inference but also to continuously adapt to new data for on-device lifelong learning.
Figure.1 : Algorithm and system co-design reduces the training memory from 303MB (PyTorch) to 149KB with the same transfer learning accuracy, leading to 2300x reduction. The numbers are measured with MobilenetV2-w0.35, batch size 1 and resolution 128x128. It can be deployed to a microcontroller with 256KB SRAM.
Figure.2 : Measured peak memory and latency: (a) Sparse update with our graph optimization reduces the measured peak memory by 20-21x. (b) Graph optimization consistently improves the peak memory (c) Sparse update with our operators achieves 23-25x faster training speed. For all numbers, we choose the config that achieves the same accuracy as full update.
@article{lin2022ondevice,
title = {On-Device Training Under 256KB Memory},
author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song},
booktitle={Annual Conference on Neural Information Processing Systems (NeurIPS)},
year = {2022}
}
We thank National Science Foundation (NSF), MIT-IBM Watson AI Lab, MIT AI Hardware Program, Amazon, Intel, Qualcomm, Ford, Google for supporting this research.
