Dynamic machine learning based matching of nonvolatile processor microarchitecture to harvested energy profile
Dynamic machine learning based matching of nonvolatile processor microarchitecture to harvested energy profile
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DOI：
10.1109/iccad.2015.7372634
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Kaisheng Ma;Xueqing Li;Yongpan Liu;Jack Sampson;Yuan Xie;N. Vijaykrishnan
中科院分区：
文献类型：
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作者：
Kaisheng Ma;Xueqing Li;Yongpan Liu;Jack Sampson;Yuan Xie;N. Vijaykrishnan
没有储能装置的能量采集系统必须有效地利用波动和微弱的电源，以确保最大的计算进度。虽然更简单的处理器可以在电源较弱的情况下实现更高的开启潜力，但更强大的处理器可以利用更多的能量。早期的工作表明，不同复杂程度的非易失性微体系结构最适合不同的电源，甚至同一电源中的不同路径。在这项工作中，我们提出了一种将所有非流水线(NP)、N级流水线(NSP)和无序(O00)核集成在一起的动态非易失性微体系结构。还集成了神经网络机器学习算法，动态调整微体系结构，实现最大前进进度。这种集成的解决方案可以实现相当于基线NP体系结构的2.4倍(OOO核心的1.82倍)的前向进度。
Energy harvesting systems without an energy storage device have to efficiently harness the fluctuating and weak power sources to ensure the maximum computational progress. While a simpler processor enables a higher turn-on potential with a weak source, a more powerful processor can utilize more energy that is harvested. Earlier work shows that different complexity levels of nonvolatile microarchitectures provide best fit for different power sources, and even different trails within same power source. In this work, we propose a dynamic nonvolatile microarchitecture by integrating all non-pipelined (NP), N-stage-pipeline (NSP), and Out of Order (OoO) cores together. Neural network machine learning algorithms are also integrated to dynamically adjust the microarchitecture to achieve the maximum forward progress. This integrated solution can achieve forward progress equal to 2.4× of the baseline NP architecture (1.82× of an OoO core).