CCF:SMALL:TIMING VARIATION RESILIENT SIGNAL PROCESSING: HARDWARE-ASSISTED CROSS-LAYER ADAPTATION

CCF:SMALL:时序变化弹性信号处理:硬件辅助跨层自适应

基本信息

  • 批准号:
    1319783
  • 负责人:
  • 金额:
    $ 24.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-09-01 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

In this research, a new vertically integrated cross-layer timing variation resilience methodology at the algorithm, microarchitecture and circuit levels, with "hardware-assistance" from the latter two levels is proposed. This addresses the effects of process variations and random delay defects in modern deeply scaled technologies as well as the effects of electrical bugs. At the highest level of the layer stack, the project considers algorithmic level workload adaptation as well as adaptation to intermittent errors in the underlying hardware due to low-power/high-speed pipeline and arithmetic unit operation. A key contribution of this research is a novel way to accurately determine when the logic and arithmetic units of pipeline stages have finished computation. This uses concepts from wave pipelined operation of logic circuits and allows activity completion detection within nearly a single or a few gate delays. Such completion sensing allows pipeline stages to "borrow" or "lend" time much more effectively than currently used synchronously clocked pipelines. In addition, backup error detection mechanisms allow the processor pipeline to operate reliably even when there is some kind of malfunction in the completion sensing circuitry. A vertically integrated control algorithm is used to modulate power vs. performance vs. timing error resilience at the circuit, microarchitecture and algorithm (video compression) levels to deliver the desired quality of service at the required video throughput with minimum power consumption. Through this research effort, the development of courses at GaTech in embedded DSP design and test, and yield management research under extreme process variations will be greatly facilitated. The PIs will develop a set of teaching materials on power management and error resilience in real-time digital signal processing systems. The PIs will make maximum effort to involve undergraduate students from the Summer Undergraduate Research Experience for minorities (SURE) program in the proposed research. It will also be possible to involve senior undergraduate project students in this research through targeted advisement. They will participate in H.O.T. Days@ Georgia Tech, a one-week long summer program designed to introduce high school students to electrical and computer engineering concepts. The key involvement will be in working with robots (LEGO Mindstorm, simple functions). Both Georgia Tech and Auburn University aggressively encourage participation of undergraduate students, as well as women and minorities in research. Auburn's participation in the project will also improve the research capabilities of Alabama, an EPSCOR state. Additionally, several master's students from the Historically Black Tuskegee University located near Auburn will take graduate courses at Auburn University. The best prepared among these students will be encouraged to join the project and Ph.D. programs at the participating universities. Thus, funding for this project will support the goals of recruiting more U.S. citizens, women and minorities to graduate programs.
在这项研究中,提出了一种在算法、微架构和电路层面垂直集成的跨层时序变化弹性方法,并从后两个层面提供“硬件辅助”。这解决了现代深度扩展技术中工艺变化和随机延迟缺陷的影响以及电气错误的影响。 在层堆栈的最高层,该项目考虑算法级工作负载适应以及对底层硬件由于低功耗/高速管道和算术单元操作而产生的间歇性错误的适应。这项研究的一个关键贡献是一种新的方法来准确确定流水线阶段的逻辑和算术单元何时完成计算。这使用了逻辑电路的波流水线操作的概念,并允许在几乎单个或几个门延迟内检测活动完成。这种完成感测允许流水线级比当前使用的同步时钟流水线更有效地“借用”或“借出”时间。此外,备份错误检测机制允许处理器管道可靠地运行,即使在完成感测电路中存在某种故障时也是如此。垂直集成控制算法用于在电路、微架构和算法(视频压缩)级别上调节功耗与性能与时序错误恢复能力,从而以最低的功耗在所需的视频吞吐量下提供所需的服务质量。通过这项研究工作,GaTech 的嵌入式 DSP 设计和测试以及极端工艺变化下的良率管理研究课程的开发将得到极大的促进。 PI 将开发一套关于实时数字信号处理系统中的电源管理和错误恢复能力的教材。 PI 将尽最大努力让少数民族暑期本科生研究体验 (SURE) 项目的本科生参与拟议的研究。通过有针对性的建议,也可以让高年级本科生项目学生参与这项研究。他们将参加H.O.T. Days@ Georgia Tech,一个为期一周的暑期项目,旨在向高中生介绍电气和计算机工程概念。主要参与将是与机器人的合作(LEGO Mindstorm,简单的功能)。佐治亚理工学院和奥本大学都积极鼓励本科生以及女性和少数族裔参与研究。奥本参与该项目还将提高 EPSCOR 州阿拉巴马州的研究能力。此外,来自位于奥本附近的历史黑人塔斯基吉大学的几名硕士生将在奥本大学攻读研究生课程。我们将鼓励这些学生中准备最充分的学生加入该项目并攻读博士学位。参与大学的课程。因此,该项目的资金将支持招募更多美国公民、女性和少数族裔参加研究生项目的目标。

项目成果

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Abhijit Chatterjee其他文献

The effect of occlusive and unocclusive exposure to xylene and benzene on skin irritation and molecular responses in hairless rats
闭塞和非闭塞接触二甲苯和苯对无毛大鼠皮肤刺激和分子反应的影响
  • DOI:
    10.1007/s00204-004-0629-1
  • 发表时间:
    2005-04-13
  • 期刊:
  • 影响因子:
    6.1
  • 作者:
    Abhijit Chatterjee;R. Babu;E. Ahaghotu;M;ip Singh;ip
  • 通讯作者:
    ip
Percutaneous absorption and skin irritation upon low-level prolonged dermal exposure to nonane, dodecane and tetradecane in hairless rats
无毛大鼠低水平长时间皮肤接触壬烷、十二烷和十四烷后的经皮吸收和皮肤刺激
  • DOI:
    10.1191/0748233704th197oa
  • 发表时间:
    2004-07-01
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    R. Babu;Abhijit Chatterjee;E. Ahaghotu;Mandip Singh
  • 通讯作者:
    Mandip Singh
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks
TESDA:基于变换的深度神经网络攻击统计检测
  • DOI:
    10.1007/s10994-021-06068-6
  • 发表时间:
    2021-10-16
  • 期刊:
  • 影响因子:
    0
  • 作者:
    C. Amarnath;Aishwarya H. Balwani;Kwondo Ma;Abhijit Chatterjee
  • 通讯作者:
    Abhijit Chatterjee
Error Resilience in Deep Neural Networks Using Neuron Gradient Statistics
使用神经元梯度统计的深度神经网络的错误恢复能力
Gaussian Control Barrier Functions: Non-Parametric Paradigm to Safety
高斯控制屏障函数:非参数安全范式
  • DOI:
    10.1109/access.2022.3206372
  • 发表时间:
    2022-03-29
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Mouhyemen Khan;Tatsuya Ibuki;Abhijit Chatterjee
  • 通讯作者:
    Abhijit Chatterjee

Abhijit Chatterjee的其他文献

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{{ truncateString('Abhijit Chatterjee', 18)}}的其他基金

Collaborative Research: An Effective and Efficient Low-Cost Alternate to Cell Aware Test Generation for Cell Internal Defects
协作研究:针对电池内部缺陷的电池感知测试生成有效且高效的低成本替代方案
  • 批准号:
    2331002
  • 财政年份:
    2023
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
CCF: Small: Real-Number Function Encoding Driven Error Resilient Signal Processing and Control: Application to Nonlinear Systems from Adaptive Filters to DNNs
CCF:小型:实数函数编码驱动的误差弹性信号处理和控制:从自适应滤波器到 DNN 的非线性系统应用
  • 批准号:
    2128419
  • 财政年份:
    2021
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
CCF: Small: Real-Number Function Encoding Driven Error Resilient Signal Processing and Control: Application to Nonlinear Systems from Adaptive Filters to DNNs
CCF:小型:实数函数编码驱动的误差弹性信号处理和控制:从自适应滤波器到 DNN 的非线性系统应用
  • 批准号:
    2128419
  • 财政年份:
    2021
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
EFFICIENT TESTING AND POST-MANUFACTURE TUNING OF BEAMFORMING MIMO WIRELESS COMMUNICATION SYSTEMS: ALGORITHMS AND INFRASTRUCTURE
波束赋形 MIMO 无线通信系统的高效测试和制造后调整:算法和基础设施
  • 批准号:
    1815653
  • 财政年份:
    2018
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
SaTC: STARSS: Trojan Detection and Diagnosis in Mixed-Signal Systems Using On-The-Fly Learned, Precomputed and Side Channel Tests
SaTC:STARSS:使用动态学习、预计算和侧通道测试的混合信号系统中的特洛伊木马检测和诊断
  • 批准号:
    1441754
  • 财政年份:
    2014
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
Collaborative Research:Cross-Domain Built-In Tuning of Advanced Mixed- Signal Radio-Frequncy Systems-on-Chip For Yield Recovery and Electrical Stress Management
合作研究:先进混合信号射频片上系统的跨域内置调谐,用于良率恢复和电应力管理
  • 批准号:
    1407542
  • 财政年份:
    2014
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
CCF: Small: Learning Assisted Induced Noise and Error Tolerant Digital and Analog Filters Using Reduced-Distance Codes
CCF:小型:使用缩短距离代码的学习辅助感应噪声和容错数字和模拟滤波器
  • 批准号:
    1421353
  • 财政年份:
    2014
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
CIF: Imperfection-Resilient Scalable Digital Signal Processing Algorithms and Architectures Using Significance Driven Computation
CIF:使用重要性驱动计算的不完美弹性可扩展数字信号处理算法和架构
  • 批准号:
    0916270
  • 财政年份:
    2009
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant
Collaborative Resarch: Targeting Multi-Core Clock Performance Gains in the Face of Extreme Process Variations
协作研究:在极端工艺变化的情况下瞄准多核时钟性能增益
  • 批准号:
    0903454
  • 财政年份:
    2009
  • 资助金额:
    $ 24.5万
  • 项目类别:
    Standard Grant

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