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.

翻译：混合信号神经形态处理器利用脉冲神经网络（SNNs）中的稀疏异步计算，为边缘推理工作负载提供极低功耗运行。然而，由于模拟硬件参数的可控性有限，以及制造非理想性导致的模拟电路参数和动力学无意识变化，在这类设备上部署鲁棒的应用变得复杂。本文提出了一种新颖的方法，用于离线训练和部署脉冲神经网络至混合信号神经形态处理器DYNAP-SE2。该方法利用基于梯度的训练，通过混合信号设备的可微仿真，结合无监督权重量化方法优化网络参数。训练过程中的参数噪声注入增强了对量化和设备失配效应的鲁棒性，使该方法成为在硬件约束和非理想性下实际应用的有前景候选方案。本研究扩展了开源SNN深度学习库Rockpool，支持混合信号SNN动力学的精确仿真。我们的方法简化了神经形态社区的开发与部署流程，使混合信号神经形态处理器更易于研究人员和开发者使用。