VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks
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 prop...
| Authors | Dimitrios Sekertzis, Giorgos Dimitrakopoulos |
|---|---|
| Primary category | cs.NE |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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