FPGA Implementation of Bio-Inspired Computing Architecture Based on Simple Neuron Model
The processor architecture families are under power, thermal, and area constraints and considers allowing optimization of chip throughput for a specific device technology by an appropriate model for performance, memory sub-systems and interconnects for various technology architectures. The system level design framework provides the best possible design tradeoff for different technology and choice of processor architectures. The aim of Bio-Inspired engineering is to reverse engineer the human brain using VLSI as well as analog electronics circuits. This research manuscript depicts the demonstration of braininspired computing architecture based on Leaky-Integrate-and- File (LIF) neuron model for neuromorphic computing system is implemented on the Field Programmable Gate Array (FPGA). The reconfigurable and event driven parameters are considered to design a field-programmable neuromorphic computing system. The register transfer logic (RTL) results of implementation and hardware synthesis are presented as a proof of concept. The neuron model is explored in Xilinx-ISE software with utilizing Verilog code, considering digital implementation, aiming at highspeed low-cost large-scale systems.
Neuromorphic Computing, Processor Architecture, Neuron, Synapse, FPGA.