课题基金 / 基金详情

Thermal-Aware GPU-Based Design Engine for On-Chip Power Delivery in Power-Efficient Multi-Core Chips

Thermal-Aware GPU-Based Design Engine for On-Chip Power Delivery in Power-Efficient Multi-Core Chips
基于热感知 GPU 的设计引擎,用于高能效多核芯片的片上供电
批准号:
0903485
负责人:
Peng Li
金额:
$25.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-01 至 2013-08-31

项目摘要

项目成果

Peng Li的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to address the computational challenges in multi-core power distribution design by leveraging recent advances in single-instruction multiple-data (SIMD) graphics processing units (GPUs). The approach is to develop a massively parallel GPU-accelerated design engine to facilitate the analysis, design and verification of power-gated multi-core on-chip power delivery networks encompassing both electrical and thermal integrity issues. Intellectual Merit: Aggressive fine-grained power gating is essential to pushing the performance vs. power envelope of current and future multi-core chip designs. This need introduces significant challenges in the design and verification of power delivery networks under complex power gating scenarios. While the recent GPU advances provide a potentially promising computing solution, the effective use of such SIMD compute power requires rethinking computed-aided design. In this work, GPU-specific computing paradigms, algorithms and implementations will be developed to address multi-core power distribution design and associated full-chip thermal challenges via efficient parallel computing on low-cost SIMD graphics processors. Broader Impacts: This work exploits recent SIMD GPU based massively parallel platforms for addressing CAD challenges. The acquired experience is likely to contribute to computing advances in other science and engineering fields. The PI will promote the research participation from undergraduate students and students from underrepresented groups. The outcomes of this work will be integrated into the PI's graduate-level VLSI courses to provide educational and research experiences to students. The developed algorithms and methodologies will be disseminated in the research community at large and major semiconductor and EDA companies for potential industrial application.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
SHF: Small: Semi-supervised Learning for Design and Quality Assurance of Integrated Circuits
SHF: Small: Methods and Architectures for Optimization and Hardware Acceleration of Spiking Neural Networks
Towards fault-tolerant, reliable, efficient, and economical DC-DC conversion for DC grid (FREE-DC)
  • 批准号:
    EP/X031608/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $37.69万
  • 财政年份:
    2023
  • 负责人:
    Peng Li
  • 依托单位:
CAREER: Compact digital biosensing system enabled by localized acoustic streaming
海外基金