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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview

Ilkin Aliyev, Kama Svoboda, Tosiron Adegbija, Jean-Marc Fellous

TL;DR

This paper addresses the challenge of delivering energy-efficient AI via sparsity-aware hardware-software co-design for Spiking Neural Networks (SNNs). It surveys how sparsity representations, neuron models, input encoding, and training methods interact with hardware architectures to influence efficiency, including static and dynamic sparsity and their practical hardware implications. The authors provide empirical insights on how hyperparameters and encoding choices affect sparsity and performance, and they review a range of sparsity-aware hardware accelerators and design-space explorations. The work offers a cohesive framework for developing embedded neuromorphic systems that fully exploit sparse SNN computations, highlighting both the potential gains and the challenges that remain in standardization, tooling, and scalability.

Abstract

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.

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview

TL;DR

This paper addresses the challenge of delivering energy-efficient AI via sparsity-aware hardware-software co-design for Spiking Neural Networks (SNNs). It surveys how sparsity representations, neuron models, input encoding, and training methods interact with hardware architectures to influence efficiency, including static and dynamic sparsity and their practical hardware implications. The authors provide empirical insights on how hyperparameters and encoding choices affect sparsity and performance, and they review a range of sparsity-aware hardware accelerators and design-space explorations. The work offers a cohesive framework for developing embedded neuromorphic systems that fully exploit sparse SNN computations, highlighting both the potential gains and the challenges that remain in standardization, tooling, and scalability.

Abstract

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.
Paper Structure (25 sections, 3 equations, 3 figures, 2 tables)

This paper contains 25 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background on Spiking Neural Networks
  3. Neuron models
  4. Spatiotemporal dynamics
  5. Learning in SNNs
  6. Input encoding
  7. Applications
  8. Neurobiological Foundations of Sparsity
  9. Sparse neural coding
  10. Structural sparsity
  11. Sparsity, plasticity, and learning
  12. Computational models of sparsity
  13. Understanding the Dynamics of Sparsity in Practical SNNs
  14. Sparsity in the LIF and Lapicque neuron models
  15. Practical impacts of model hyperparameters on sparsity
  16. ...and 10 more sections

Figures (3)

  • Figure 1: SNN neurons integrate incoming spikes $x$ with corresponding synaptic weights $w$ to generate output spikes $y$ every time the integrated membrane potential reaches a threshold.
  • Figure 2: LIF neuron model cross sweep results for $\beta$ and $\theta$ parameters.
  • Figure 3: Lapicque neuron model exploration for $\beta$ and $\theta$.