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，电子芯片的制造具有独特的个性，这源于无法精确制造其底层电路，也无法创建软件来控制由此产生的不确定性。可以使用复杂的测试方法来确定性能最佳的系统，但这将导致巨大的成本。电子芯片的个性还受到其环境和使用的影响，由于两者都可以随着时间的推移而波动，因此系统个性也可以。系统也会老化，并可能意外磨损。这些先天和后天的影响使得设计一个电子芯片或系统非常困难，它将为所有可能的个性提供最佳运作。为了应对这一挑战，本研究将开发“片内统计学习”（SLIC）。SLIC是电子芯片/系统设计的整体方法，基于在线不断学习关键个性特征，用于将系统进化到优化性能的状态。SLIC不仅将优化电子芯片性能，而且还将降低成本，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，特别是移动的系统，对功率有严格的约束，这需要从根本上重新考虑如何实现学习，特别是因为系统本身既执行学习又使用所得到的知识。因此，可以想象，一些学习任务将需要定制硬件;对于其他人，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）将开发用于芯片的新的学习算法，以优化每个系统级别和跨级别的操作;（ii）将开发、设计和实现学习和适应系统个性的自我进化系统;以及最后（iii）使用不同的设计来增强性能以及降低功耗-将展示来自医疗和消费电子领域的驱动器应用。本研究的优点在于基于SLIC设计范式创建和展示了一个自进化系统的想法。具体而言，SLIC将提供一种全面的方法，通过学习来应对系统堆栈各级的不确定性，从而在系统需求随时间变化时实现性能、功耗和可靠性的权衡。

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.