Neuromorphic Computing Architecture Paradigms and Silicon Implementations: A Comparative Analysis

International Journal of Computer Science and Technology | Vol. 4, No. 12, December 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000227

Article Type: Review Paper

Title: Neuromorphic Computing Architecture Paradigms and Silicon Implementations: A Comparative Analysis

Names of Authors: Kofi Mensah¹, Yuki Tanaka²

Authors’ Affiliations: ¹Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; ²Advanced VLSI Systems Laboratory, Tokyo Tech, Tokyo, Japan

Abstract: Von Neumann processing architectures encounter severe energy bottlenecks and memory speed limitations when running modern deep learning model structures. Neuromorphic computing presents an alternative framework by designing silicon architectures that mimic biological neural networks. This review paper provides a comparative analysis of modern neuromorphic silicon implementations, including Intel Loihi, IBM TrueNorth, and SpiNNaker. We evaluate each hardware platform across operational vectors such as synaptic density, energy dissipation, and on-chip learning efficiency. The study aggregates performance datasets compiled from 40 experimental research papers published over the past six years. Our findings show that neuromorphic designs cut power usage by up to 1,000x compared to classic GPU architectures when processing sparse temporal streams. However, configuring non-von Neumann systems requires specialized programming tools and remains limited by hardware constraints during dense matrix calculations. This paper presents a taxonomical framework to guide hardware designers in matching neuromorphic platforms with specific edge applications.

Keywords: Neuromorphic Computing, Non-Von Neumann Architecture, Spiking Neural Networks, Silicon Implementations, Energy Efficiency, VLSI

Manuscript Timeline: Received: March 20, 2023; Revised: May 12, 2023; Accepted: June 28, 2023; Published: December 10, 2023

Citation: Mensah, K., & Tanaka, Y. (2023). Neuromorphic Computing Architecture Paradigms and Silicon Implementations: A Comparative Analysis. International Journal of Computer Science and Technology, 4(12), 1–8. DOI: 10.46882/2023/IJCST/000227

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