Neuromorphic Computing: Mimicking the Human Brain
DOI:
https://doi.org/10.15680/IJCTECE.2020.0304002Keywords:
Neuromorphic computing, spiking neural networks, memristors, brain-inspired architecture, cognitive computing, energy-efficient computing, hardware-software, integration, artificial intelligence, robotics, cognitive systemsAbstract
Neuromorphic computing is an interdisciplinary field that seeks to emulate the structure and function of the human brain in computational systems. Unlike traditional von Neumann architectures, which separate memory and processing units, neuromorphic systems integrate memory and processing to mimic the brain's parallel, distributed, and energy-efficient processing capabilities. This approach leverages hardware and software inspired by biological neural networks to perform cognitive tasks such as perception, learning, and decision-making. The primary motivation behind neuromorphic computing is to overcome the limitations of conventional computing systems in handling complex, real time, and adaptive tasks. By modeling neurons and synapses using specialized hardware components like memristors and spiking neural networks (SNNs), neuromorphic systems can process information in a manner akin to biological systems. This paradigm shift has the potential to revolutionize fields such as artificial intelligence, robotics, and cognitive computing. This paper provides a comprehensive overview of neuromorphic computing, including its historical development, key principles, hardware implementations, and emerging applications. It also discusses the challenges and future directions of the field, emphasizing the need for interdisciplinary collaboration to advance neuromorphic technologies.
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