Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding

• URL: http://arxiv.org/abs/2501.07952v1
• Date: Tue, 14 Jan 2025 09:09:08 GMT
• Title: Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
• Authors: Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr,
• Abstract summary: Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency. Two important questions when implementing SNNs are how to best encode existing data into spike trains and how to efficiently process these spike trains in hardware. This paper addresses both of these problems by incorporating the encoding into the learning process, thus allowing the network to learn the spike encoding alongside the weights.
• Score: 0.3749861135832073
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• Abstract: Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to traditional Artificial Neural Networks (ANNs). Two important questions when implementing SNNs are how to best encode existing data into spike trains and how to efficiently process these spike trains in hardware. This paper addresses both of these problems by incorporating the encoding into the learning process, thus allowing the network to learn the spike encoding alongside the weights. Furthermore, this paper proposes a hardware architecture based on a recently introduced differential-time representation for spike trains allowing decoupling of spike time and processing time. Together these contributions lead to a feedforward SNN using only Leaky-Integrate and Fire (LIF) neurons that surpasses 99% accuracy on the MNIST dataset while still being implementable on medium-sized FPGAs with inference times of less than 295us.

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