In this paper, the foundations of neuromorphic computing, spiking neural networks (SNNs) and memristors, are analyzed and discussed. Neuromorphic computing is then applied to FPGA design for digital signal processing (DSP). Finite impulse response (FIR) and infinite impulse response (IIR) filters are implemented with and without neuromorphic computing in Vivado using Verilog HDL. The results suggest that neuromorphic computing can provide low-latency and synaptic plasticity thereby enabling continuous on-chip learning. Due to their parallel and event-driven nature, neuromorphic computing can reduce power consumption by eliminating von Neumann bottlenecks and improve efficiency, but at the cost of reduced numeric precision.

翻译：本文分析并探讨了神经形态计算、脉冲神经网络（SNNs）与忆阻器的基本原理。随后，将神经形态计算应用于面向数字信号处理（DSP）的FPGA设计。在Vivado环境中使用Verilog HDL，分别采用神经形态计算方式与未采用该方式实现了有限脉冲响应（FIR）与无限脉冲响应（IIR）滤波器。结果表明，神经形态计算能够提供低延迟与突触可塑性，从而实现持续的片上学习。得益于其并行性与事件驱动特性，神经形态计算能够通过消除冯·诺依曼瓶颈来降低功耗并提升效率，但代价是数值精度的降低。