Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview
This is an incremental overview paper that aims to guide the development of embedded neuromorphic systems for energy-efficient AI applications.
The paper explores hardware-software co-design approaches for Spiking Neural Networks (SNNs) to leverage sparsity for ultra-low-power AI, analyzing how sparsity representation, hardware architectures, and training techniques influence efficiency.
Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.