Enabling Incremental Training with Forward Pass for Edge Devices

TL;DR

This paper introduces a resource-efficient evolutionary strategy method that enables incremental training of neural networks on edge devices without backpropagation, allowing adaptation and recovery in resource-constrained environments.

Contribution

It presents a novel incremental training technique using evolutionary strategies suitable for inference hardware, demonstrated on FPGA with minimal resource overhead.

Findings

Successfully retrained a quantized MNIST network after input noise injection

Implemented the method on FPGA with less than 1% resource utilization

Enabled training on inference hardware without backpropagation

Abstract

Deep Neural Networks (DNNs) are commonly deployed on end devices that exist in constantly changing environments. In order for the system to maintain it's accuracy, it is critical that it is able to adapt to changes and recover by retraining parts of the network. However, end devices have limited resources making it challenging to train on the same device. Moreover, training deep neural networks is both memory and compute intensive due to the backpropagation algorithm. In this paper we introduce a method using evolutionary strategy (ES) that can partially retrain the network enabling it to adapt to changes and recover after an error has occurred. This technique enables training on an inference-only hardware without the need to use backpropagation and with minimal resource overhead. We demonstrate the ability of our technique to retrain a quantized MNIST neural network after injecting noise to the input. Furthermore, we present the micro-architecture required to enable training on HLS4ML (an inference hardware architecture) and implement it in Verilog. We synthesize our implementation for a Xilinx Kintex Ultrascale Field Programmable Gate Array (FPGA) resulting in less than 1% resource utilization required to implement the incremental training.