Despite best efforts, electronic chips are manufactured with unique personalities that stem from the inability to precisely fabricate their underlying circuits and to create software for controlling the resulting uncertainty. It is possible to use sophisticated test methods to identify the best-performing systems, but this would result in significant cost. An electronic chip's personality is further shaped by its environment and usage, and since both can fluctuate over time, so can the system personality. Systems also grow old and can wear out unexpectedly. These nature and nurture influences make it extremely difficult to design an electronic chip or system that will operate optimally for all possible personalities.
To address this challenge, this research will develop "statistical learning in-chip" (SLIC). SLIC is a holistic approach to electronic chip/system design based on continuously learning key personality traits on-line, for evolving a system to a state that optimizes performance. SLIC will not only optimize electronic chip performance but will also reduce costs since systems that were before deemed to have weak personalities at the time of fabrication can now be recovered through the use of SLIC. Integrated systems, especially mobile systems, have stringent constraints on power that necessitate a fundamental re-thinking of how to implement learning, especially since the system itself both performs the learning and uses the resulting knowledge. Therefore, it is conceivable that some learning tasks will require custom hardware; for others, software or even cloud-based solutions may be possible and/or necessary. In this work, (i) new learning algorithms for use in the chip that optimize operation at every system level and across levels will be developed; (ii) self-evolving systems that learn and adapt to the personality of a system will be developed, designed and implemented; and finally (iii) enhanced performance as well as reduced power consumption using diverse design-driver applications from the medical and consumer electronics fields will be demonstrated. The merit of this research centers on the idea of creating and demonstrating a self-evolving system based on the SLIC design paradigm. Specifically, SLIC will provide a comprehensive approach for coping with uncertainty at all levels of the system stack through learning that enables tradeoffs in performance, power, and reliability as demands on the system change over time.
