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Optimizing Memory Latency and Bandwidth of Spiking Neural Network Accelerators on FPGA via Sparse Hashing
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Optimizing Memory Latency and Bandwidth of Spiking Neural Network Accelerators on FPGA via Sparse Hashing

Shadi Matinizadeh, M. L. Varshika and Anup Das
Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design, pp 1-9
26 Oct 2025
DOI: https://doi.org/10.1109/ICCAD66269.2025.11240985

Abstract

Bandwidth Bloom filter Field programmable gate arrays Hardware Hash functions Memory management memory over-provisioning Optimization Organizations Resource management sparse hashing Sparse matrices Spiking neural networks spiking neural networks (SNNs)
Spiking Neural Networks (SNNs) exploit the natural temporal sparsity by processing discrete events. Introducing additional weight sparsity can further improve their energy efficiency when implemented in hardware. We propose SPARSH, a novel memory organization technique that uses sparse hashing to allow efficient storage and fast access while minimizing memory over-provisioning. Through SPARSH, we make the following four key contributions. First, we introduce an efficient hash function to evenly distribute nonzero weights to buckets that are optimized for an FPGA memory system and placed in a compact memory space to achieve storage efficiency. Second, we use a controller that integrates a Bloom filter to detect sparsity and skip memory accesses for weights and activations that are zero, and for neurons that are in refractory state. This improves memory bandwidth utilization. Third, we propose a scheduler that improves memory latency by prioritizing accesses that hit in the same bucket over other accesses. Finally, we propose an algorithm to select the design parameters of SPARSH based on the sparsity of a target SNN. We implement SPARSH for a recent SNN accelerator on a Virtex UltraScale and evaluate using seven SNNs models. We show that SPARSH reduces memory over-provisioning by 3.2×, access latency by 77%, and bandwidth utilization by 68% with a marginal increase in resource utilization.

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