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Cross-Layer Accelerated Self-Healing:
Circadian Rythms for Resilient Electronic Systems

A great challenge in recent chip design is coping with process, voltage, temperature and aging (PVTA) variations.  These variations require increased design margins or alternative circuit designs to minimize their effects, which often reduce power, performance, and area (PPA) metrics.  Aging in particular causes a gradual degradation of these metrics while a circuit is under stress and eventually leads to catastrophic failure.  Since this degradation does not scale down with technology size, it is becoming a bigger and bigger concern as its effect on electronic circuits increases.  However, many mechanisms involved in aging show evidence that a certain amount of recovery can be achieved by removing the conditions that caused stress in the first place.  These mechanisms are not well-understood and so their recovery processes cannot be effectively taken advantage of.

This project approaches the issue by developing cross-layer models for aging degradation and recovery, from device-level models for mechanisms such as bias temperature instability (BTI) to architecture level models for power and performance degradation.  With these models, CLASH proposes an idea of periodic active recovery inspired by circadian rhythms in biological systems where an animal regularly undergoes a period of deep rejuvenation, or sleeps.  The modern idea of "sleeping" for an electronic system is one where it is merely inactive; by adding environmental conditions such as elevated temperature, recovery can be accelerated and the system can be recovered beyond what it would normally experience.

Models for aging and recovery will be developed for memory (Flash and SRAM), FPGA, and processors.  These models, along with experimental data from production and custom chips, will be used to demonstrate the effects of aging as well as improvements in lifetime and PPA metrics that can be achieved using periodic active recovery.  This project is funded by Intel, AMD, and the National Science Foundation.

Ongoing Research Highlights:

Team Members:
  • Professor Mircea Stan
  • Alec Roelke
  • Xinfei Guo