SHF: Large: High-Performance, Low-Power, Self-Evolving Integrated Systems through Statistical Learning in Chip (SLIC)

尽管尽了最大的努力，但由于无法精确地制造其底层电路和创建控制由此产生的不确定性的软件，电子芯片被制造出独特的个性。可以使用复杂的测试方法来确定性能最好的系统，但是这将导致巨大的成本。电子芯片的个性进一步受到其环境和使用的影响，由于两者都可能随着时间的推移而波动，因此系统的个性也是如此。系统也会变老，可能会意外地磨损。这些先天和后天的影响使得设计一种电子芯片或系统对所有可能的个性都能进行最佳操作变得极其困难。为了应对这一挑战，本研究将开发“芯片内统计学习”（SLIC）。SLIC是一种基于持续在线学习关键人格特征的电子芯片/系统设计的整体方法，旨在将系统进化到最佳性能状态。SLIC不仅可以优化电子芯片性能，还可以降低成本，因为以前在制造时被认为具有弱个性的系统现在可以通过使用SLIC来恢复。集成系统，特别是移动系统，对能力有严格的限制，需要从根本上重新思考如何实现学习，特别是因为系统本身既执行学习，又使用由此产生的知识。因此，可以想象，一些学习任务将需要定制硬件；对于其他人来说，软件甚至基于云的解决方案可能是可能的和/或必要的。在这项工作中，(i)将开发用于优化每个系统级别和跨级别操作的芯片中的新学习算法；（ii）能够学习和适应系统个性的自我进化系统将被开发、设计和实施；最后（iii）将展示使用来自医疗和消费电子领域的各种设计驱动应用程序提高性能并降低功耗。本研究的优点在于以SLIC设计范例为基础，创造并展示一个自我演化的系统。具体来说，SLIC将提供一种全面的方法，通过学习来应对系统堆栈所有级别的不确定性，随着系统需求的变化，可以在性能、功率和可靠性方面进行权衡。

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.