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Overoptimistic voltage scaling in pre-error AVS systems and learning-based alleviation

机译：在错误的AVS系统和基于学习的缓解中的过度优化电压缩放

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In-situ timing error detection and correction mechanisms (such as Razor) monitor the performance of actual datapaths, and are believed more resilient in adaptive voltage scaling (AVS) systems, especially when considering local variations. However, Razor has serious hold time problems, of which the overwhelming buffer padding makes it infeasible in advanced process technologies. Pre-error (or in-situ canary) detection was then proposed as an alternative. In addition, sophisticated error correction is no longer needed accordingly. The Markov chain model was proposed by independent researchers to design the pre-error AVS controller to explicitly trade quality for energy, where the input patterns are assumed to have a Gaussian delay distribution. For error-tolerant applications where few errors are acceptable, pre-error AVS is shown more efficient than Razor-based approaches. In this paper, a pre-error AVS system has been constructed on a 28nm FPGA platform with programmable power supply. It is observed that when the delay distributions are time-varying and non-Gaussian, overoptimistic voltage scaling (OVS) can occur and may lead to serious problems in pre-error AVS. To resolve the OVS problem, we propose to insert certain logics to collect the delay criticality information, which is fed into a Q-learning model to create the learning-based AVS. Experimental results show that the proposed scheme saves 10.50% power while reducing 0.16% error rate for random inputs and saves 12.51% power while reducing 0.06% error rate for non-random inputs, compared with the original pre-error AVS that assumes a static Gaussian delay distribution.
机译：原位定时错误检测和校正机制（例如剃刀）监控实际数据路径的性能，并且在适应性电压缩放（AVS）系统中，特别是在考虑局部变化时更具弹性。然而，剃刀具有严重的保持时间问题，其中压倒性的缓冲填充使得在先进的过程技术中不可行。然后提出了误差（或原位金丝雀）检测作为替代方案。此外，不再需要复杂的纠错。独立研究人员提出了马尔可夫链模型，以设计预先误差AVS控制器以明确地进行能量的贸易质量，其中假设输入图案具有高斯延迟分布。对于耐堵塞应用程序的差错应用，错误的误差是基于剃刀的方法更有效地显示出错误的AV。在本文中，在具有可编程电源的28nm FPGA平台上构造了一个错误的AVS系统。观察到，当延迟分布是时变的并且非高斯时，可能发生优化电压缩放（OV）并且可能导致错误错误AVS中的严重问题。为了解决OVS问题，我们建议插入某些逻辑以收集延迟关键性信息，该信息被馈送到Q学习模型中以创建基于学习的AVS。实验结果表明，该方案可节省10.50％的功率，同时降低随机输入的0.16％差错率，并节省了12.51％的功率，同时降低了非随机输入的0.06％的错误率，与假冒高斯的原始预误差AV相比延迟分布。

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