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

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 fr...

Full description

Saved in:
Bibliographic Details
Main Authors: Lim, S. H., Lim, Y. W., Mashohor, S., Kamsani, N. A., Sidek, R. M., Hashim, S. J., Rokhani, F. Z.
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Integrated circuit modeling
Libraries
Neural Network
Optimization
Power Optimization
Predictive models
Standard Cell Library
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2159-2160
DOI:10.1109/PRIMEASIA.2017.8280374