Cao, Linan, Bale, Simon Jonathan and Trefzer, Martin Albrecht orcid.org/0000-0002-6196-6832 (2021) Multi-objective Digital Design Optimisation via Improved Drive Granularity Standard Cells. Ieee transactions on circuits and systems i-Regular papers. pp. 4660-4671. ISSN 1549-8328
Abstract
To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink the technology nodes and electronic design automation (EDA) vendors offer hierarchical design flows to decompose systems into smaller blocks. However, such a staged design methodology consists of various levels of abstraction, where margins will be accumulated and result in degradation of the overall design quality. This limits the full use of capabilities of both the process technology and EDA tools. In this work, a study of drive granularity of standard cells is performed and an interpolation method is proposed for drive option expansion within original cell libraries. These aim to investigate how industrial synthesis tools deal with the drive strength selection using different granularity sets. In addition, a fully-automated, multi-objective (MO) EDA digital flow is introduced for power, performance, area (PPA) optimisation based on drive strength refinement. This population-based search method better handles the increased difficulty of cell selection when using larger logic libraries, producing better optimised solutions than standard tool flow in this case. The achieved experimental results demonstrate how the improved drive granularity cells overall enhance the quality of designs and how a significant improvement in trading off PPA is achieved by the MOEDA flow.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | circuits,systems,digital design,electronic design automation EDA,multi-objective optimisation |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 Mar 2022 15:10 |
Last Modified: | 23 Jan 2025 00:26 |
Published Version: | https://doi.org/10.1109/TCSI.2021.3109239 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TCSI.2021.3109239 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184954 |