Vlsi implementation of neural systems
Neuromorphic Computing Systems for Industry 4.0, ISSN: 2327-3453, Page: 94-116
2023
- 44Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Captures44
- Readers44
- 44
Book Chapter Description
A unique strategy for optimum multi-objective optimization for VLSI implementation of artificial neural network (ANN) is proposed. This strategy is efficient in terms of area, power, and speed, and it has a good degree of accuracy and dynamic range. The goal of this research is to find the sweet spot where area, speed, and power may all be optimised in a very large-scale integration (VLSI) implementation of a neural network (NN). The design should also allow for the dynamic reconfiguration of weight, and it should be very precise. The authors also use a 65-nm CMOS fabrication method to produce the circuits, and these results show that the suggested integral stochastic design may reduce energy consumption by up to 21% compared to the binary radix implementation, without sacrificing accuracy.
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