深度学习技术在集成电路设计中的应用

Q2 Energy
Lihua Dai, Ben Wang, Xuemin Cheng, Qin Wang, Xinsen Ni
{"title":"深度学习技术在集成电路设计中的应用","authors":"Lihua Dai,&nbsp;Ben Wang,&nbsp;Xuemin Cheng,&nbsp;Qin Wang,&nbsp;Xinsen Ni","doi":"10.1186/s42162-024-00380-w","DOIUrl":null,"url":null,"abstract":"<div><p>This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00380-w.pdf","citationCount":"0","resultStr":"{\"title\":\"The application of deep learning technology in integrated circuit design\",\"authors\":\"Lihua Dai,&nbsp;Ben Wang,&nbsp;Xuemin Cheng,&nbsp;Qin Wang,&nbsp;Xinsen Ni\",\"doi\":\"10.1186/s42162-024-00380-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.</p></div>\",\"PeriodicalId\":538,\"journal\":{\"name\":\"Energy Informatics\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1186/s42162-024-00380-w.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s42162-024-00380-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Energy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Informatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1186/s42162-024-00380-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0

摘要

本研究探讨了集成电路(IC)设计中电路布局优化这一复杂挑战,其主要目标是实现功耗、性能指标和芯片面积之间的最佳平衡(统称为 PPA 优化)。这项任务十分复杂,已演变成一个多约束条件下的多维问题,因此有必要探索先进的方法。为了应对这些挑战,我们的研究引入了深度学习技术,作为彻底改变电路布局优化的创新策略。具体来说,我们采用卷积神经网络(CNN)来开发优化布局策略、性能预测模型以及故障检测和实时监控系统。这些方法利用了深度学习模型从高维数据表示中学习的能力,并能有效处理多个约束条件。广泛的案例研究和严格的实验验证证明了我们提出的深度学习驱动方法的有效性。结果表明,优化效率显著提高,平均功耗降低了 120%,延迟降低了 1.5%。此外,功率预测的平均绝对误差降低到了 3%,证明预测能力得到了显著提高。对比分析充分说明,深度学习方法在多个维度上都优于传统技术。我们的研究结果强调了深度学习在实现更高精度预测、展示更强的泛化能力、促进卓越设计质量以及最终提高用户满意度方面的潜力。这些进步不仅验证了深度学习在集成电路设计优化中的适用性,还为未来解决电路布局优化固有的多维挑战铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The application of deep learning technology in integrated circuit design

This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信