A High-Throughput Spiking Neural Network Processor Enabling Synaptic Delay Emulation

📝 Original Info

• Title: A High-Throughput Spiking Neural Network Processor Enabling Synaptic Delay Emulation
• ArXiv ID: 2511.01158
• Date: 2025-11-03
• Authors: 제공되지 않음 (논문에 저자 정보가 포함되지 않았습니다)

📝 Abstract

Synaptic delay has attracted significant attention in neural network dynamics for integrating and processing complex spatiotemporal information. This paper introduces a high-throughput Spiking Neural Network (SNN) processor that supports synaptic delay-based emulation for edge applications. The processor leverages a multicore pipelined architecture with parallel compute engines, capable of real-time processing of the computational load associated with synaptic delays. We develop a SoC prototype of the proposed processor on PYNQ Z2 FPGA platform and evaluate its performance using the Spiking Heidelberg Digits (SHD) benchmark for low-power keyword spotting tasks. The processor achieves 93.4% accuracy in deployment and an average throughput of 104 samples/sec at a typical operating frequency of 125 MHz and 282 mW power consumption.

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