Neuromorphic Computing - Hardware and Algorithms: Exploring neuromorphic computing hardware and algorithms inspired by the brain's architecture for efficient and adaptive computing

Authors

  • Dr. Astrid Lwoga Professor of Information Systems, University of Dar es Salaam, Tanzania Author

Keywords:

Neuromorphic computing, hardware, algorithms

Abstract

Neuromorphic computing, inspired by the human brain's architecture, presents a paradigm shift in computing, offering efficient and adaptive solutions to complex problems. This paper provides a comprehensive review of neuromorphic computing, focusing on both hardware and algorithms. We begin by discussing the motivation behind neuromorphic computing and its key principles. We then delve into the hardware aspects, examining various neuromorphic computing platforms, such as memristors, spiking neural networks (SNNs), and neuromorphic chips. Next, we explore the algorithms used in neuromorphic computing, including learning rules, synaptic plasticity, and event-driven computation. We also discuss the challenges and future directions of neuromorphic computing, highlighting its potential impact on artificial intelligence and cognitive computing.

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References

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Prabhod, Kummaragunta Joel. "Advanced Machine Learning Techniques for Predictive Maintenance in Industrial IoT: Integrating Generative AI and Deep Learning for Real-Time Monitoring." Journal of AI-Assisted Scientific Discovery 1.1 (2021): 1-29.

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

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Published

14-06-2023

How to Cite

[1]
Dr. Astrid Lwoga, “Neuromorphic Computing - Hardware and Algorithms: Exploring neuromorphic computing hardware and algorithms inspired by the brain’s architecture for efficient and adaptive computing”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 318–325, Jun. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/32

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