Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors
This work addresses deployment difficulties for researchers and developers using mixed-signal neuromorphic processors, though it is incremental as it builds on existing training and simulation techniques.
The paper tackles the challenge of deploying robust spiking neural networks (SNNs) on mixed-signal neuromorphic processors like DYNAP-SE2, which suffer from hardware non-idealities and limited controllability, by introducing a gradient-based training method with differentiable simulation and noise injection, resulting in improved robustness under hardware constraints.
Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, as well as unintended parameter and dynamical variations of analog circuits due to fabrication non-idealities. Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2. The methodology utilizes gradient-based training using a differentiable simulation of the mixed-signal device, coupled with an unsupervised weight quantization method to optimize the network's parameters. Parameter noise injection during training provides robustness to the effects of quantization and device mismatch, making the method a promising candidate for real-world applications under hardware constraints and non-idealities. This work extends Rockpool, an open-source deep-learning library for SNNs, with support for accurate simulation of mixed-signal SNN dynamics. Our approach simplifies the development and deployment process for the neuromorphic community, making mixed-signal neuromorphic processors more accessible to researchers and developers.