TL;DR: A sleep-inspired, replay-driven memory consolidation-based temporal learning framework that reconstructs context from replayed memory during brief sleep periods for temporal context learning in resource-constrained edge systems is proposed.
In edge computing applications, storing long temporal sequences is memory and energy intensive, while sensory data arrives sequentially, requiring online learning. Although online updates reduce storage requirements, they lack access to broader temporal context needed for accurat…
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-…