TL;DR: This work presents a flexible, end-to-end workflow for the training, optimization, and deployment of SNNs across multiple neuromorphic hardware systems, with an emphasis on extensibility to future neuromorphic platforms.
Surrogate gradient methods have emerged as the dominant approach for enabling backpropagation for spiking neural networks (SNNs). However, many implementations are tightly coupled to target hardware platforms, constraining flexibility. In this work, we present a flexible, end-to-…
TL;DR: CRISP (Compiled RISP) is a neuromorphic simulator based on RISP, which compiles a given network into a minimal sequence of machine code for the desired platform entirely at runtime.
Simulators for spiking neural networks lie on the critical path between a researcher with an idea and a working network. Many of the current simulators have various implementation inefficiencies that hamper their performance across a wide variety of tasks. CRISP (Compiled RISP) i…