Generating power-optimal standard cell library specification using neural network technique

Citation

Lim, Sze Huang and Lim, Yang Wei and Mashohor, Syamsiah and Kamsani, Noor Ain and Mohd Sidek, Roslina and Hashim, Shaiful Jahari and Rokhani, Fakhrul Zaman (2017) Generating power-optimal standard cell library specification using neural network technique. In: 2017 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia), 31 Oct.-2 Nov. 2017, Kuala Lumpur, Malaysia. (pp. 101-104).

Abstract

In VLSI semi-custom design approach, power-optimal standard cell library selection for a given block design requires time-consuming iterative processes. This paper presents a framework to select a standard cell library that can result in near-optimal power while satisfying targeted frequency. The framework relies on neural network model to quickly predict the total power of a block design associated with a given standard cell library in order to speed up the synthesis process. The experimental result based on various synthesized benchmark circuits demonstrated the effectiveness of proposed framework for near-optimal standard cell library specification.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1109/PRIMEASIA.2017.8280374
Publisher:	IEEE
Keywords:	Neural network; Power optimization; Standard cell library
Depositing User:	Nabilah Mustapa
Date Deposited:	10 May 2019 08:28
Last Modified:	10 May 2019 08:28
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/PRIMEASIA.2017.8280374
URI:	http://psasir.upm.edu.my/id/eprint/68267
Statistic Details:	View Download Statistic

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