Fpga Implementation Of Brain-inspired Neuromorphic Computing Circuits

Abstract

Brain-inspired Neuromorphic Computing Has Emerged As An Efficient Approach For Implementing Cognitive And Learning-based Systems With Low Power Consumption And High Parallelism. Unlike Conventional Computing Architectures, Neuromorphic Systems Emulate The Structure And Functionality Of Biological Neural Networks Using Spiking Neurons And Synaptic Connections. This Work Presents The FPGA Implementation Of A Brain-inspired Neuromorphic Computing Circuit Designed To Model Basic Neural Processing Behavior In Hardware. The Proposed Architecture Employs Neuron And Synapse Models Mapped Onto FPGA Resources To Achieve Real-time Operation And Reconfigurability. The Design Is Implemented Using Hardware Description Language And Validated Through Simulation And FPGA Synthesis. Experimental Results Demonstrate Correct Neural Signal Processing, Efficient Resource Utilization, And Suitability For Real-time Neuromorphic Applications. The Proposed FPGA-based Neuromorphic Circuit Provides A Flexible And Scalable Platform For Developing Brain-inspired Computing Systems

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