A Review on Hybrid Network on Chip

Sujatha, Machineni Sanjeevappa; Jayagowri, R.

doi:10.1109/icctct.2018.8551009

Cited by 2 publications

(1 citation statement)

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“…Traditional von Neumann based general-purpose computers suffer from the limitation of data transmission speed between the CPU and memory, which hinders their further development in our current era of data explosion. Emerging technologies such as in-memory computing, [228] and network-on-chip [229,230] will play an important role in the design of neuromorphic computing systems in the future. iii.…”

Section: Discussionmentioning

confidence: 99%

Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems

et al. 2020

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The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.

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Section: Discussionmentioning

confidence: 99%