چکیده مقاله
Neuromorphic computing, inspired by the human brain's neural architecture, offers a transformative approach to artificial intelligence AI by enabling ultra efficient, event driven processing through Spiking Neural Networks SNNs Integrating this paradigm with the flexible, open source RISC V instruction set architecture ISA has catalyzed advances in low power, real time edge AI systems This review explores key theoretical frameworks, such as the Leaky Integrate and Fire neuron model and Spike Dependent Synaptic Plasticity, and the development of neuromorphic ISA extensions that accelerate sparse, asynchronous computations critical for energy constrained environments It traces the evolution from early analog inspired neuromorphic hardware to modern RISC V based neuromorphic processors with custom accelerators and scalable Network on Chip architectures The discussion highlights hardware software co design efforts addressing analog noise, hardware variability, and scalability, essential for robust, adaptive edge intelligence Real world applications in autonomous robotics, healthcare monitoring, and industrial anomaly detection demonstrate the practical impact of these systems The article concludes that RISC V based neuromorphic computing represents a paradigm shift toward sustainable, high performance edge AI by combining biological inspiration with adaptable processor design, and underscores the importance of ongoing research in device calibration, noise robustness, and ISA standardization to fully realize this technology’s potential
کلیدواژهها
نویسندگان
Mohammad Sadra Lotfi Gharaei
۱Associate Degree Student in Electronics, Department of Electrical Engineering, Shahid Montazeri Technical and Vocational University, Mashhad, Iran
Nima Shayesteh Zarrin
۲Adjunct Lecturer, Department of Electrical Engineering, Shahid Montazeri Technical and Vocational University, Mashhad, Iran
شیوه ارجاع
Lotfi Gharaei, Mohammad Sadra and Shayesteh Zarrin, Nima,1404,RISC-V-Based Neuromorphic Computing: Spiking Neural Networks for Ultra-Efficient Edge AI,28th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات بیست و هشتمین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک25 آذر 1404 · شیروان