VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks

Dimitrios Sekertzis; Giorgos Dimitrakopoulos

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arXiv · 2606.20675

VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks

Dimitrios Sekertzis·Giorgos Dimitrakopoulos

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Abstract

Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for edge AI, making them attractive for hardware acceleration. However, deploying dense SNNs on FPGAs is constrained by limited on-chip memory for synaptic weight storage. To address this bottleneck, we propose VQ4SNN, a hardware-aware architecture that reduces memory requirements through Vector Quantization (VQ)-based weight sharing. To the best of our knowledge, this is the first application of VQ to pipelined spatial-dataflow SNN accelerators on FPGAs. VQ4SNN replaces conventional weight storage with a two-level memory organization consisting of compact pointers and a shared codebook of quantized weight vectors. The proposed design integrates FPGA-aware memory mapping with analytical VQ parameter selection, enabling efficient deployment on such accelerators while preserving inference accuracy. The experimental results show a reduction of 52-61% in the total number of BRAMs compared to the state-of-the-art uncompressed FPGA SNNs without increasing overall logic utilization.

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Dimitrios Sekertzis, Giorgos Dimitrakopoulos. 2026-06-12. VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks. https://arxiv.org/abs/2606.20675

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