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

• URL: http://arxiv.org/abs/2606.20675v1
• Date: Fri, 12 Jun 2026 15:33:36 GMT
• Title: VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks
• Abstract summary: VQ4SNN is a hardware-aware architecture that reduces memory requirements through Vector Quantization (VQ)-based weight sharing.<n>VQ4SNN replaces conventional weight storage with a two-level memory organization consisting of compact pointers and a shared codebook of quantized weight vectors.
• Score: 0.3580891736370874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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