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

摘要

While the notion of self-awareness has a long history in biology, psychology, medicine, engineering and (more recently) computing, we are seeing the emerging need for self-awareness in the context of complex Systems-on-Chip that must address the often conflicting requirements of performance, resiliency, energy, cost, etc. in the face of highly dynamic operational behaviors coupled with process, environment, and workload variabilities. Unlike traditional Systems-on-Chip (SoCs), self-aware SoCs must deploy an intelligent co-design of the control, communication, and computing infrastructure that interacts with the physical environment in real-time in order to modify the systems behavior so as to adaptively achieve desired objectives and Quality-of-Service (QoS). Self-aware SoCs require a combination of ubiquitous sensing and actuation, health-monitoring, and self-learning to enable the SoCs adaptation over time and space. This special session targets self-learning and self-awareness in two domains. The first one is a self-learning runtime reliability prediction approach by reusing Design-for-Test (DfT) infrastructure. The other one discusses real-time systems and applications to wireless communication, signal processing and control.