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System-Physician-on-a-Chip (SPOC): Chip Health-Monitoring Infrastructure IP and Run-Time Adaptation

片上系统医生 (SPOC)：芯片健康监控基础设施 IP 和运行时适配

基本信息

批准号：
269744693
负责人：
Professor Mehdi B. Tahoori, Ph.D.
金额：
--
依托单位：
Institut für Technische Informatik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/269744693?language=en
关键词：
System Physician Chip SPOC Health

项目摘要

Design-time solutions and guard-bands for resilience are no longer sufficient for nanoscale integrated circuits (ICs). Each chip, due to process variations, is born with a unique personality (nature), and because of operating conditions, environment, and workload, grows uniquely (nurture). This project is motivated by the need to guarantee that each system, despite different nature and nurture, has an acceptable behavior (resilience). Resilience has been defined as the persistence of performance level that can justifiably be trusted in the presence of change. Hence static solutions based on pre-determined adaptation strategies cannot provide adequate resilience as systems evolve with time. While todays ICs incorporate a large number of sensors (thermal, voltage, delay, etc.) for runtime monitoring, breakthroughs are needed to extract useful information from sensor data, perform real-time analysis, and make decisions about online adaptation. Appropriate reasoning methods are also needed to deal with inconsistent or contradictory sensor data due to stress-, process-, and workload-induced spatiotemporal variations. It is important to predict system state so that countermeasures can be taken before a failure occurs. The proposed research is focused on data-driven techniques for guiding dynamic adaptation policies. This level of dynamic decision-making and prediction-based control is a significant step forward towards resilient systems. The intellectual merit lies in the advancement of data analytics solutions for reasoning about on-chip behavior, the integration of prediction-based adaptation, and the update of adaptation strategies based on success, or lack thereof, of past adaptation decisions. Specific tasks include: 1. Monitoring of performance, power, and reliability: In addition to the use of on-chip sensors, the research will involve the reuse and redesign of BIST infrastructure to accurately monitor events such as voltage droop and aging by aggregating

设计时解决方案和保护带的弹性不再足以满足纳米级集成电路（IC）。由于工艺的差异，每个芯片都具有独特的个性（自然），并且由于操作条件，环境和工作负载，独特地成长（培育）。这个项目的动机是需要保证每个系统，尽管不同的性质和培养，有一个可接受的行为（弹性）。复原力被定义为在发生变化的情况下可以信赖的业绩水平的持久性。因此，基于预先确定的适应战略的静态解决方案不能提供足够的复原力，因为系统随着时间的推移而演变。虽然今天的IC集成了大量的传感器（热，电压，延迟等）。对于运行时监控，需要突破性地从传感器数据中提取有用的信息，执行实时分析，并做出关于在线适配的决策。还需要适当的推理方法来处理不一致或矛盾的传感器数据，由于应力，过程和工作负载引起的时空变化。重要的是预测系统状态，以便在故障发生之前采取对策。拟议的研究重点是指导动态适应政策的数据驱动技术。这种动态决策和基于预测的控制水平是迈向弹性系统的重要一步。其智力价值在于数据分析解决方案的进步，用于推理片上行为，集成基于预测的自适应，以及基于过去自适应决策的成功或失败更新自适应策略。具体任务包括：1.性能、功耗和可靠性的监控：除了使用片上传感器，研究还将涉及BIST基础设施的重用和重新设计，通过聚合来准确监控电压下降和老化等事件

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Self-awareness and self-learning for resiliency in real-time systems

实时系统弹性的自我意识和自我学习

DOI：
10.1109/iolts.2015.7229845
发表时间：
2015
期刊：
2015 IEEE 21st International On-Line Testing Symposium (IOLTS)
影响因子：
0
作者：
M.B. Tahoori;A. Chatterjee;K. Chakrabarty;A. Koneru;A. Vijayan;D. Banerjee
通讯作者：
D. Banerjee

Workload-Aware Static Aging Monitoring and Mitigation of Timing-Critical Flip-Flops

DOI：
10.1109/tcad.2017.2778254
发表时间：
2018-10
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Arunkumar Vijayan;S. Kiamehr;Fabian Oboril;K. Chakrabarty;M. Tahoori
通讯作者：
Arunkumar Vijayan;S. Kiamehr;Fabian Oboril;K. Chakrabarty;M. Tahoori

Online Soft-Error Vulnerability Estimation for Memory Arrays and Logic Cores

DOI：
10.1109/tcad.2017.2706558
发表时间：
2018-02
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Arunkumar Vijayan;S. Kiamehr;Mojtaba Ebrahimi;K. Chakrabarty;M. Tahoori
通讯作者：
Arunkumar Vijayan;S. Kiamehr;Mojtaba Ebrahimi;K. Chakrabarty;M. Tahoori

Runtime Identification of Hardware Trojans by Feature Analysis on Gate-Level Unstructured Data and Anomaly Detection