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

2025/06/16 by Filippo Marostica, Alessio Carpegna, Marostica, Filippo +5
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #CCD and CMOS Imaging Sensors #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function

paper · doi:10.48550/arxiv.2506.13268

openalex publication_date 2025/06/16 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Survey of Spiking Neural Network Accelerator on FPGA
• Training Spiking Neural Networks Using Lessons From Deep Learning
• FPGA Implementation of Simplified Spiking Neural Network
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
• Converting Static Image Datasets to Spiking Neuromorphic Datasets Using\n Saccades
• The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
• A 128×128 120 dB 15 μs Latency Asynchronous Temporal Contrast Vision Sensor
• A quantitative description of membrane current and its application to conduction and excitation in nerve

Related

• Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side
• Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
• A Unified Evaluation Framework for Spiking Neural Network Hardware Accelerators Based on Emerging Non-Volatile Memory Devices
• Spiking Neural Networks: The Future of Brain-Inspired Computing
• Energy-Oriented Computing Architecture Simulator for SNN Training