Spiking Neural Network Models Analysis on Field Programmable Gate Arrays

Spiking Neural Networks (SNNs) are a type of neural network designed to replicate biological neural networks more closely by using discrete spikes to transmit information. Unlike traditional etworks, SNNs incorporate time by relying on the precise timing of spikes for neuron-to-neuron communication. This reduces hardware omplexity, as it only requires one-bit logic, making SNNs ideal for hardware integration. This study assesses the performance of several SNN models for hardware implementation, focusing on resource utilization, speed, and power consumption. Verilog was used for the hardware design, and the simulations were run in Vivado. The emulation experiments were conducted on the Basys3 FPGA board to validate our findings. Our analysis indicates that simpler models like Leaky Integrate and Fire (LIF) and Non-linear Integrateand- Fire (NLIF) are highly efficient, with low resource and power requirements, making them suitable for resource-constrained environments. More complex models like Hodgkin-Huxley (HH) and Izhikevich (IZH) provide detailed neuronal dynamics but at a higher resource cost. Our implementations exhibit notable improvements across several metrics compared to previouswork. This analysis equips researchers with the necessary information to make informed decisions about which neuron model best meets their application needs, whether prioritizing speed, efficiency, or biological accuracy.