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

Researchers propose VQ4SNN, a hardware-efficient architecture that uses vector quantization to reduce memory requirements for spiking neural networks on FPGAs by 52-61% without sacrificing inference accuracy. This innovation addresses a critical bottleneck in deploying dense SNNs on edge hardware, combining weight-sharing techniques with FPGA-aware memory optimization.

VQ4SNN represents a meaningful technical advancement in edge AI acceleration, tackling a genuine constraint that has limited SNN deployment at scale. Spiking neural networks offer inherent energy advantages over traditional deep learning approaches, but their practical implementation on resource-constrained hardware has been hampered by memory bottlenecks. This work bridges that gap through vector quantization, a compression technique that groups similar weights into shared codebook entries, allowing the hardware to store pointers instead of full weight matrices.

The research builds on growing momentum in neuromorphic computing, where SNNs are increasingly recognized as viable alternatives to conventional neural networks for latency-sensitive and power-constrained applications. FPGA acceleration has become a primary deployment target for edge AI because FPGAs offer flexible hardware reconfiguration without the power consumption of GPUs. The integration of VQ techniques into spatial-dataflow SNN accelerators marks the first purposeful application of this compression method to this specific architectural paradigm.

For the hardware acceleration and edge AI sectors, this work has direct implications. A 52-61% reduction in block RAM (BRAM) usage means developers can deploy larger models or more instances on the same hardware, directly improving performance-per-watt metrics that drive edge computing economics. The approach maintains inference accuracy while reducing silicon area, translating to lower costs and faster deployment cycles.

Future development hinges on whether these techniques generalize across different SNN architectures and quantization levels. Open questions remain about how VQ4SNN performs with different training methodologies and whether the codebook overhead becomes problematic at larger scales.

• →VQ4SNN reduces FPGA memory requirements for SNNs by 52-61% using vector quantization and weight-sharing techniques
• →First application of vector quantization to pipelined spatial-dataflow SNN accelerators represents a novel technical contribution
• →Memory reduction achieved without increasing logic utilization, enabling denser model deployment on edge hardware
• →Hardware-aware design integrates analytical VQ parameter selection with FPGA memory mapping for practical efficiency gains
• →Addresses critical bottleneck in neuromorphic computing deployment, improving economics of edge AI acceleration