CAREER: Predicting Timing Violations: A New Direction for Robust System Design

职业:预测时序违规:鲁棒系统设计的新方向

基本信息

  • 批准号:
    1253024
  • 负责人:
  • 金额:
    $ 46.61万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-03-01 至 2021-02-28
  • 项目状态:
    已结题

项目摘要

Timing violations, an artifact of rapid technology scaling, embody a central reliability challenge in microprocessor design. A vast body of existing techniques in the landscape of timing violations fall under two broad categories: reactive and proactive. In both of these schemes, the lack of sufficient time in identifying an upcoming timing violation limits the scope of corrective techniques, incurring a large recovery overhead, or loss in fault coverage, respectively. Using a cross-layer analysis combining information from the application, architecture and circuit layers, this CAREER project demonstrates that timing violations can be predicted several clock cycles early. Early prediction of timing violations can offer a vast leverage in robust system design, opening up a promising direction for future systems research. In this paradigm, microprocessors can operate at a tighter frequency, where predictable errors frequently occur and are tolerated with minimal performance loss. Such a system design style can reshape physical design algorithms, boosting the energy efficient frontiers for microprocessor components. Research in the nascent area of cross-layer design will enable circuit designers and system architects to increase collaborative design, and develop affordable, energy efficient and reliable computer systems. The rapid growth and evolution of integrated circuits creates a huge demand of Computer Engineering skill sets for addressing upcoming challenges. The CAREER project seeks to create a stream of future engineers capable of tackling growing unreliability in integrated circuits through focused knowledge dissemination. Undergraduates, women and minorities will be actively sought for participation through the existing platforms at Utah State University such as the Society of Women Engineers (SWE), Engineering State and Center for Women and Gender (CWG) programs.
时序违规是快速技术扩展的产物,体现了微处理器设计中的核心可靠性挑战。大量的现有技术在时序违规的景观分为两大类:反应和主动。在这两种方案中,缺乏足够的时间来识别即将到来的时序违规限制了纠正技术的范围,分别导致了大的恢复开销或故障覆盖率的损失。使用跨层分析结合信息的应用程序,架构和电路层,这个CAREER项目表明,时序违规可以预测几个时钟周期提前。时序违规的早期预测可以在鲁棒系统设计中提供巨大的杠杆作用,为未来的系统研究开辟了一个有前途的方向。在这种模式下,微处理器可以在更严格的频率下运行,其中经常发生可预测的错误,并且可以容忍最小的性能损失。这样的系统设计风格可以重塑物理设计算法,提高微处理器组件的能效前沿。跨层设计这一新兴领域的研究将使电路设计师和系统架构师能够增加协作设计,并开发出价格合理、节能和可靠的计算机系统。集成电路的快速增长和发展为应对即将到来的挑战创造了对计算机工程技能的巨大需求。CAREER项目旨在通过集中的知识传播,创造一批能够解决集成电路日益增长的不可靠性的未来工程师。将通过犹他州州立大学现有的平台,如女工程师协会(SWE),工程州和妇女与性别中心(CWG)计划,积极寻求本科生,妇女和少数民族的参与。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
UPTPU: Improving Energy Efficiency of a Tensor Processing Unit through Underutilization Based Power-Gating
  • DOI:
    10.1109/dac18074.2021.9586224
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Pramesh Pandey;N. D. Gundi;Koushik Chakraborty;Sanghamitra Roy
  • 通讯作者:
    Pramesh Pandey;N. D. Gundi;Koushik Chakraborty;Sanghamitra Roy
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Sanghamitra Roy其他文献

Maximising energy efficiency in 3D multicore systems: a formalised approach
最大限度提高 3D 多核系统的能源效率:形式化方法
  • DOI:
    10.1080/00207217.2012.687183
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    1.3
  • 作者:
    Sanghamitra Roy;Koushik Chakraborty
  • 通讯作者:
    Koushik Chakraborty
TITAN: Uncovering the Paradigm Shift in Security Vulnerability at Near-Threshold Computing
TITAN:揭示近阈值计算安全漏洞的范式转变
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    5.9
  • 作者:
    Prabal Basu;Pramesh Pandey;Aatreyi Bal;Chidhambaranathan Rajamanikkam;Koushik Chakraborty;Sanghamitra Roy
  • 通讯作者:
    Sanghamitra Roy
The everyday struggles of accessing public transport for women in the first- and last-mile stretches in Kolkata
加尔各答女性在第一英里和最后一英里使用公共交通的日常困难
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    6.1
  • 作者:
    Sanghamitra Roy;Ajay Bailey;Femke van Noorloos
  • 通讯作者:
    Femke van Noorloos
Probabilistic Verification for Reliability of a Two-by-Two Network-on-Chip System
二乘二片上网络系统可靠性的概率验证
Understanding the barriers affecting women's mobility in the first- and last-mile stretches in low- and middle-income countries: A systematic review
  • DOI:
    10.1016/j.jtrangeo.2024.104036
  • 发表时间:
    2024-12-01
  • 期刊:
  • 影响因子:
  • 作者:
    Sanghamitra Roy;Ajay Bailey;Femke van Noorloos
  • 通讯作者:
    Femke van Noorloos

Sanghamitra Roy的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Sanghamitra Roy', 18)}}的其他基金

CNS Core: Small: Ultra Low Power Hardware AI Accelerator for Training at the Edge
CNS 核心:小型:用于边缘训练的超低功耗硬件 AI 加速器
  • 批准号:
    2106237
  • 财政年份:
    2021
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Standard Grant
CSR: Small: DARP: Promoting Energy Efficient System Design Through a Dynamically Adaptable Resilient Pipeline
CSR:小型:DARP:通过动态适应性弹性管道促进节能系统设计
  • 批准号:
    1421022
  • 财政年份:
    2014
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Standard Grant
CSR:Small:Employing Design Automation to Build Foundations for Holistic Multicore Design
CSR:小:利用设计自动化为整体多核设计奠定基础
  • 批准号:
    1117425
  • 财政年份:
    2011
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Standard Grant

相似海外基金

Predicting how the inducible defences of large mammals to human predation shape spatial food web dynamics
预测大型哺乳动物对人类捕食的诱导防御如何塑造空间食物网动态
  • 批准号:
    EP/Y03614X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Research Grant
NSF PRFB FY 2023: Considering evolutionary responses to temperature variability when predicting risk to climate change and disease in amphibians
NSF PRFB 2023 财年:在预测气候变化和两栖动物疾病风险时考虑对温度变化的进化反应
  • 批准号:
    2305659
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Fellowship Award
Predicting effects of interannual variability in climate and drought on plant community outcomes, resilience, and soil carbon using temporally replicated grassland reconstructions
使用临时复制的草地重建来预测气候和干旱的年际变化对植物群落结果、恢复力和土壤碳的影响
  • 批准号:
    2343738
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Continuing Grant
Predicting abnormalities in abdominal organs through prognostic factors extracted from image data
通过从图像数据中提取的预后因素来预测腹部器官的异常
  • 批准号:
    24K21121
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
R-Map - Mapping, understanding, assessing and predicting the effects of remote working arrangements in urban and rural areas
R-Map - 绘制、理解、评估和预测城乡地区远程工作安排的影响
  • 批准号:
    10106145
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    EU-Funded
Predicting future biodiversity of ecosystem service providers in Japan using new approaches to quantify and reduce uncertainty
使用量化和减少不确定性的新方法来预测日本生态系统服务提供者的未来生物多样性
  • 批准号:
    24K09176
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
CPS: Medium: Federated Learning for Predicting Electricity Consumption with Mixed Global/Local Models
CPS:中:使用混合全局/本地模型预测电力消耗的联合学习
  • 批准号:
    2317079
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Standard Grant
Predicting emergence risk of future zoonotic viruses through computational learning
通过计算学习预测未来人畜共患病毒的出现风险
  • 批准号:
    MR/X019616/1
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Fellowship
Early Life Antecedents Predicting Adult Daily Affective Reactivity to Stress
早期生活经历预测成人对压力的日常情感反应
  • 批准号:
    2336167
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Standard Grant
CAREER: Marine Debris at Coastlines: predicting sources from drift, dispersion, and beaching via experiments and multiscale stochastic models
职业:海岸线的海洋碎片:通过实验和多尺度随机模型预测漂移、分散和搁浅的来源
  • 批准号:
    2338221
  • 财政年份:
    2024
  • 资助金额:
    $ 46.61万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了