The demise of Dennard’s scaling has created both power and utilization wall challenges for computer systems. As transistors operating in the near-threshold region are able to obtain flexible trade-offs between power and performance, it is regarded as an alternative solution to the scaling challenge. A reduction in supply voltage will nevertheless generate significant reliability challenges, while maintaining an error-free system that generates high costs in both performance and energy consumption. The main purpose of research on computer architecture has therefore shifted from performance improvement to complex multi-objective optimization. In this paper, we propose a three-dimensional optimization approach which can effectively identify the best system configuration to establish a balance among performance, energy, and reliability. We use a dynamic programming algorithm to determine the proper voltage and approximate level based on three predictors: system performance, energy consumption, and output quality. We propose an output quality predictor which uses a hardware/software co-design fault injection platform to evaluate the impact of the error on output quality under near-threshold computing (NTC). Evaluation results demonstrate that our approach can lead to a 28% improvement in output quality with a 10% drop in overall energy efficiency; this translates to an approximately 20% average improvement in accuracy, power, and performance.

Dennard扩展规模的消亡给计算机系统带来了功耗和利用率方面的难题。由于工作在接近阈值区域的晶体管能够在功率和性能之间取得灵活的折衷，因此被认为是解决规模挑战的替代解决方案。然而，降低电源电压将带来巨大的可靠性挑战，同时保持一个无错的系统，这会在性能和能耗上产生高昂的成本。因此，计算机体系结构研究的主要目的已经从性能改进转向复杂的多目标优化。在本文中，我们提出了一种三维优化方法，该方法可以有效地识别最佳系统配置，从而在性能，能耗和可靠性之间建立平衡。我们使用动态编程算法，基于三个预测指标确定适当的电压和近似水平：系统性能，能耗和输出质量。我们提出了一种输出质量预测器，该预测器使用硬件/软件协同设计故障注入平台来评估误差对近阈值计算（NTC）下输出质量的影响。评估结果表明，我们的方法可以使输出质量提高28％，而总体能源效率降低10％。这意味着准确性，功耗和性能平均提高了约20％。我们提出了一种输出质量预测器，该预测器使用硬件/软件协同设计故障注入平台来评估误差对近阈值计算（NTC）下输出质量的影响。评估结果表明，我们的方法可以使输出质量提高28％，而总体能源效率降低10％。这意味着准确性，功耗和性能平均提高了约20％。我们提出了一种输出质量预测器，该预测器使用硬件/软件协同设计故障注入平台来评估误差对近阈值计算（NTC）下输出质量的影响。评估结果表明，我们的方法可以使输出质量提高28％，而总体能源效率降低10％。这意味着准确性，功耗和性能平均提高了约20％。