Neuromorphic RISC-V Systems for Bio-inspiredComputing Applications

Abstract

Neuromorphic computing is a paradigm based on the computational mechanisms of the human brain and hasreceived considerable attention as a real-time technique with low energy requirements. Present systems, however, arelimited in their ability to scale traditional processors to a neuromorphic architecture, leading to issues with latency,power consumption, and smooth data flow. To address these problems, this paper proposes the ACORISC-VbSNNframework, comprising a modular RISC-V architecture, Spiking Neural Networks (SNNs), and Ant ColonyOptimization (ACO). The system uses a shared-memory architecture to maximize communication between traditionaland neuromorphic processors, ensuring data is managed effectively. The postulated framework processes the sensorydata by pre-processing and encoding them using rate coding, and dynamically optimizing memory access. SNNs arealso used to process spike trains in real-time, whereas ACO is used to determine the best data paths to minimizebottlenecks. Experimental analysis shows that the system performs better, with ultra-low power consumption of 0.0095mW, very low latency of 0.000544 seconds, and 99.2 percent accuracy. These findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.