mlsanityData to 14 Oct 2026

Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons

arXiv 2506.13268 ↗First posted 16 Jun 2025cs.NE cs.AIcited by 2

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Abstract (arXiv)

This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various SNN models based on different variants of the Leaky Integrate and Fire (LIF) neuron across multiple datasets. This phase enables controlled performance assessment and informs design refinement. Our subsequent hardware phase, implemented on FPGA, validates the simulation findings and offers practical insights into design trade offs. In particular, we examine how variations in input stimuli influence key performance metrics such as latency, power consumption, energy efficiency, and resource utilization. These results yield valuable guidelines for constructing energy efficient, real time neuromorphic systems. Overall, our work bridges software simulation and hardware realization, advancing the development of next generation SNN accelerators.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 16 Jun 2025.

Builds on

10 of 19 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  3. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  4. 2018AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmarkcited by 72
  5. 2024Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edgecited by 18
  6. 2022Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networkscited by 8
  7. 2023A Survey of Spiking Neural Network Accelerator on FPGAcited by 7
  8. 2020FPGA Implementation of Simplified Spiking Neural Networkcited by 4
  9. 2024SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGAcited by 3
  10. 2023Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologiescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0
  2. 2026Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networkscited by 0