基于Cortex-M3的自动驾驶汽车,静态功率为34nW, 15.3pJ/inst。有效能量,和16%的跨过程和温度的功率变化

R. Salvador, Alberto Sanchez, Xin Fan, T. Gemmeke
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引用次数: 11

摘要

功率和能源效率是物联网soc的主要制约因素。动态电压和频率缩放(DVFS) -与自适应电压缩放(AVS)相结合-可以牺牲性能以匹配工作负载的功率。然而,由于泄漏,传统的定时关闭会导致工艺和温度之间的巨大功率变化,特别是从SS/冷到FF/热。本文提出了一种基于ARM Cortex-M3的集成稳压器AVS的单片机,该单片机实现了CPU包括8kB SRAM 34nW静态功率,15.3pJ/指令有功能量和16%的跨工艺和温度角(-40-120°C)的硅测量功率变化。数字子系统采用了一种新的复制方案,结合了复制电路和原位监视器来跟踪芯片间/芯片内变化下的关键路径。为了实现AVS,我们展示了一种新的逻辑合成方法,该方法具有每$V^{\ mathm {th}}$级别的最佳使用百分比,此时功耗在整个过程和温度上是平衡和最小化的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Cortex-M3 Based MCV Featuring AVS with 34nW Static Power, 15.3pJ/inst. Active Energy, and 16% Power Variation Across Process and Temperature
Power and energy efficiency poses a prime constraint on IoT SoCs. Dynamic voltage and frequency scaling (DVFS) - in combination with adaptive voltage scaling (AVS) - can trade performance for power matching the workload. However, classic timing closure leads to substantial power variations across process and temperature, especially from SS/Cold to FF/Hot due to leakage. This paper presents an ARM Cortex-M3 based MCU with integrated voltage regulator featuring AVS, which achieves for the CPU incl. 8kB SRAM 34nW static power, 15.3pJ/instruction active energy and 16% power variations across process and temperature (-40-120°C) corners measured in silicon. The digital subsystem employs a new replica scheme combining replica circuits and in-situ monitors to track critical paths under the inter/intra-die variations. Mandating AVS, we show a novel methodology for logic synthesis with optimal usage percentage per $V^{\mathrm{th}}$ level, at which point the power consumption is balanced and minimized across process and temperature.
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