Optimal Scheduling and Allocation for IC Design Management and Cost Reduction

Author:

Agrawal Prabhav1,Broxterman Mike2,Chatterjee Biswadeep3,Cuevas Patrick2,Hayashi Kathy H.2,Kahng Andrew B.1,Myana Pranay K.1,Nath Siddhartha1

Affiliation:

1. UC San Diego, La Jolla, CA

2. Qualcomm, Inc., Morehouse Drive, San Diego, CA

3. Qualcomm India Private Limited, Whitefield, Bangalore KA

Abstract

A large semiconductor product company spends hundreds of millions of dollars each year on design infrastructure to meet tapeout schedules for multiple concurrent projects. Resources (servers, electronic design automation tool licenses, engineers, and so on) are limited and must be shared -- and the cost per day of schedule slip can be enormous. Co-constraints between resource types (e.g., one license per every two cores (threads)) and dedicated versus shareable resource pools make scheduling and allocation hard. In this article, we formulate two mixed integer-linear programs for optimal multi-project, multi-resource allocation with task precedence and resource co-constraints. Application to a real-world three-project scheduling problem extracted from a leading-edge design center of anonymized Company X shows substantial compute and license costs savings. Compared to the product company, our solution shows that the makespan of schedule of all projects can be reduced by seven days, which not only saves ∼ 2.7% of annual labor and infrastructure costs but also enhances market competitiveness. We also demonstrate the capability of scheduling over two dozen chip development projects at the design center level, subject to resource and datacenter capacity limits as well as per-project penalty functions for schedule slips. The design center ended up purchasing 600 additional servers, whereas our solution demonstrates that the schedule can be met without having to purchase any additional servers. Application to a four-project scheduling problem extracted from a leading-edge design center in a non-US location shows availability of up to ∼ 37% headcount reduction during a half-year schedule for just one type of chip design activity.

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference48 articles.

1. P. Agrawal B. Chatterjee A. B. Kahng P. K. Myana and S. Nath. 2015. Optimal multi-tapeout project scheduling for enterprise-scale design management and cost reduction. Work-in-Progress DAC. P. Agrawal B. Chatterjee A. B. Kahng P. K. Myana and S. Nath. 2015. Optimal multi-tapeout project scheduling for enterprise-scale design management and cost reduction. Work-in-Progress DAC.

2. M. Ayala and C. Artigues. 2010. On Integer Linear Programming Formulations for the Resource-Constrained Modulo Scheduling Problem Rapport LAAS. Technical Report 10393. M. Ayala and C. Artigues. 2010. On Integer Linear Programming Formulations for the Resource-Constrained Modulo Scheduling Problem Rapport LAAS. Technical Report 10393.

3. Tight LP bounds for resource constrained project scheduling

4. D. Bienstock and M. Zuckerberg. 2009. A new LP algorithm for precedence constrained production scheduling. Optimization Online 1--33. D. Bienstock and M. Zuckerberg. 2009. A new LP algorithm for precedence constrained production scheduling. Optimization Online 1--33.

5. CROSS cyclic resource-constrained scheduling solver

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ECO-CHIP: Estimation of Carbon Footprint of Chiplet-based Architectures for Sustainable VLSI;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

2. Invited Paper: The Inevitability of AI Infusion Into Design Closure and Signoff;2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD);2023-10-28

3. The Interplay of Online and Offline Machine Learning for Design Flow Tuning;Machine Learning Applications in Electronic Design Automation;2022

4. Reducing time and effort in IC implementation: a roadmap of challenges and solutions;Proceedings of the 55th Annual Design Automation Conference;2018-06-24

5. Machine Learning Applications in Physical Design;Proceedings of the 2018 International Symposium on Physical Design;2018-03-25

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3