In the last few years, research and development on Deep Learning models and
techniques for ultra-low-power devices in a word, TinyML has mainly focused on
a train-then-deploy assumption, with static models that cannot be adapted to
newly collected data without cloud-based data collection and fine-tuning.
Latent Replay-based Continual Learning (CL) techniques[1] enable online,
serverless adaptation in principle, but so farthey have still been too
computation and memory-hungry for ultra-low-power TinyML devices, which are
typically based on microcontrollers. In this work, we introduce a HW/SW
platform for end-to-end CL based on a 10-core FP32-enabled parallel
ultra-low-power (PULP) processor. We rethink the baseline Latent Replay CL
algorithm, leveraging quantization of the frozen stage of the model and Latent
Replays (LRs) to reduce their memory cost with minimal impact on accuracy. In
particular, 8-bit compression of the LR memory proves to be almost lossless
(-0.26% with 3000LR) compared to the full-precision baseline implementation,
but requires 4x less memory, while 7-bit can also be used with an additional
minimal accuracy degradation (up to 5%). We also introduce optimized primitives
for forward and backward propagation on the PULP processor. Our results show
that by combining these techniques, continual learning can be achieved in
practice using less than 64MB of memory an amount compatible with embedding in
TinyML devices. On an advanced 22nm prototype of our platform, called VEGA, the
proposed solution performs onaverage 65x faster than a low-power STM32 L4
microcontroller, being 37x more energy efficient enough for a lifetime of 535h
when learning a new mini-batch of data once every minute.

本文提出了基于 PULP 处理器的深度学习端到端持续学习硬件 / 软件平台，利用量化和潜在重播来优化内存成本，以实现采用小型设备的实践多样性更新。