International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 4, 8 Aug. 2026
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Neuromorphic RISC-V Systems for Bio-inspired Computing Applications
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Author(s)
Yamini Devi Ykuntam
1,*
M. V. Nageswara Rao
2
Leela Kumari. B.
3
Index Terms
Neuromorphic Computing, Latency, Power Efficiency, Spiking Neural Networks, Ant Colony Optimization, RISC-V Architecture
Abstract
Neuromorphic computing is a paradigm based on the computational mechanisms of the human brain and has received considerable attention as a real-time technique with low energy requirements. Present systems, however, are limited 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-VbSNN framework, comprising a modular RISC-V architecture, Spiking Neural Networks (SNNs), and Ant Colony Optimization (ACO). The system uses a shared-memory architecture to maximize communication between traditional and neuromorphic processors, ensuring data is managed effectively. The postulated framework processes the sensory data by pre-processing and encoding them using rate coding, and dynamically optimizing memory access. SNNs are also used to process spike trains in real-time, whereas ACO is used to determine the best data paths to minimize bottlenecks. Experimental analysis shows that the system performs better, with ultra-low power consumption of 0.0095 mW, very low latency of 0.000544 seconds, and 99.2 percent accuracy. These findings indicate that the ACORISC-VbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energy-efficient, and low-latency system for real-world use.
Cite This Paper
Yamini Devi Ykuntam, M. V. Nageswara Rao, Leela Kumari. B., "Neuromorphic RISC-V Systems for Bio-inspired Computing Applications", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.4, pp. 130-149, 2026. DOI:10.5815/ijcnis.2026.04.07
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