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

arXiv 2408.14437 ↗First posted 26 Aug 2024cs.AR cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 61 references that are in the map. First posted on arXiv 26 Aug 2024.

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References · 21

  1. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  2. 2018Deep Learning in Spiking Neural Networkscited by 258
  3. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  4. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 222
  5. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  6. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 116
  7. 2019Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performancecited by 45
  8. 2020Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differencescited by 39
  9. 2017Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantizationcited by 21
  10. 2022SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networkscited by 18
  11. 2021Efficient Spiking Neural Networks with Radix Encodingcited by 12
  12. 2019Sparsity through evolutionary pruning prevents neuronal networks from overfittingcited by 11
9 more
  1. 2020Accelerated Analog Neuromorphic Computingcited by 9
  2. 2022A Time-to-first-spike Coding and Conversion Aware Training for Energy-Efficient Deep Spiking Neural Network Processor Designcited by 5
  3. 2019Additive function approximation in the braincited by 4
  4. 2022Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networkscited by 4
  5. 2022Efficient spike encoding algorithms for neuromorphic speech recognitioncited by 4
  6. 2023Sparse-firing regularization methods for spiking neural networks with time-to-first spike codingcited by 3
  7. 2024Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networkscited by 3
  8. 2024PULSE: Parametric Hardware Units for Low-power Sparsity-Aware Convolution Enginecited by 2
  9. 2023Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Acceleratorscited by 1

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