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

arXiv: https://arxiv.org/abs/2408.14437 · HTML: https://mlsanity.com/paper/2408.14437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 61 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation](https://mlsanity.com/paper/2005.01807/) (2020)
- [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023)
- [Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance](https://mlsanity.com/paper/1909.10837/) (2019)
- [Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences](https://mlsanity.com/paper/2005.02183/) (2020)
- [Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization](https://mlsanity.com/paper/1708.01001/) (2017)
- [SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks](https://mlsanity.com/paper/2204.05422/) (2022)
- [Efficient Spiking Neural Networks with Radix Encoding](https://mlsanity.com/paper/2105.06943/) (2021)
- [Sparsity through evolutionary pruning prevents neuronal networks from overfitting](https://mlsanity.com/paper/1911.10988/) (2019)
- [Accelerated Analog Neuromorphic Computing](https://mlsanity.com/paper/2003.11996/) (2020)
- [Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2203.12437/) (2022)
- [A Time-to-first-spike Coding and Conversion Aware Training for Energy-Efficient Deep Spiking Neural Network Processor Design](https://mlsanity.com/paper/2208.04494/) (2022)
- [Additive function approximation in the brain](https://mlsanity.com/paper/1909.02603/) (2019)
- [Efficient spike encoding algorithms for neuromorphic speech recognition](https://mlsanity.com/paper/2207.07073/) (2022)
- [Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding](https://mlsanity.com/paper/2307.13007/) (2023)
- [Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks](https://mlsanity.com/paper/2402.06211/) (2024)
- [Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators](https://mlsanity.com/paper/2310.16745/) (2023)
- [PULSE: Parametric Hardware Units for Low-power Sparsity-Aware Convolution Engine](https://mlsanity.com/paper/2402.06210/) (2024)

## Abstract (arXiv)

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.