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

对于纳米级集成电路（ic）来说，设计时的解决方案和保护带不再足够。每个芯片，由于工艺的变化，天生具有独特的个性（性质），并且由于操作条件，环境和工作量，生长独特（培育）。这个项目的动机是需要保证每个系统，尽管有不同的性质和培育，有一个可接受的行为（弹性）。弹性被定义为在存在变化时可以合理信任的性能水平的持久性。因此，基于预先确定的适应策略的静态解决方案不能提供足够的弹性，因为系统随着时间的推移而演变。虽然目前的集成电路集成了大量用于运行时监测的传感器（热、电压、延迟等），但在从传感器数据中提取有用信息、执行实时分析以及做出在线适应决策方面仍需取得突破。此外，还需要适当的推理方法来处理由于压力、过程和工作负载引起的时空变化而导致的不一致或矛盾的传感器数据。预测系统状态是很重要的，这样可以在故障发生之前采取对策。研究的重点是数据驱动技术，以指导动态适应政策。这种水平的动态决策和基于预测的控制是向弹性系统迈出的重要一步。智力上的优点在于数据分析解决方案的进步，用于推理芯片上的行为，基于预测的适应的集成，以及基于过去适应决策的成功或缺乏的适应策略的更新。具体任务包括：1；监测性能、功率和可靠性：除了使用片上传感器外，该研究还将涉及对BIST基础设施的重用和重新设计，以通过聚合准确地监测电压下降和老化等事件

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