A Fast Cross-Layer Dynamic Power Estimation Method by Tracking Cycle-Accurate Activity Factors With Spark Streaming
A Fast Cross-Layer Dynamic Power Estimation Method by Tracking Cycle-Accurate Activity Factors With Spark Streaming
复制标题
一种通过 Spark Streaming 跟踪周期精确活动因子的快速跨层动态功率估计方法
DOI：
10.1109/tvlsi.2021.3111000
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发表时间：
2021-09
影响因子：
2.8
通讯作者：
Longxing Shi
中科院分区：
文献类型：
--
作者：
Leilei Jin;Wenjie Fu;Ming Ling;Longxing Shi
自主功率受限系统的出现对早期设计空间探索提出了新的挑战。现有的架构级功耗评估工具由于忽略了电路级行为的特征以及工艺、电压和温度变化的影响而失去了准确性。虽然基于SPICE或黄金时间PX(PTPX)的功率估计足够准确，但它们是以较长的模拟时间为代价的，并且仅在设计流程的非常后期阶段才可用。本文提出了一种快速、准确的动态功率评估方法，在电路级对有功因子进行估计。通过提出的有效电容模型考虑了栅级工艺变化的影响。然后，通过电路的模型和输入向量来估计活动因子。输入向量由体系结构级仿真以流的形式生成。为了实现实时、高速的功耗评估，提出了一种面向海量并行的数据流框架。基于运行SPEC CPU2006基准的PULPino处理器的功能单元对跨层估计进行了验证。与使用中芯国际28 nm PDK的SPICE结果相比，我们的逐周期动态功率分析的平均误差为5.4%。同时，我们的方法实现速度比传统的PTPX模拟快65.2%，比最新的跨层评估方法快48.8%。
The advent of autonomous power-limited systems poses a new challenge for early design space exploration. The existing architecture-level power evaluation tools lose accuracy due to ignoring features of circuit-level behaviors and influences of process, voltage, and temperature variations. Although power estimations based on SPICE or PrimeTime PX (PTPX) are accurate enough, they come at the cost of long simulation time and are available only in very late phases of design flow. In this article, a fast and accurate dynamic power evaluation method is proposed, which estimates activity factors at the circuit level. The impact of process variation at the gate level is considered through the proposed effective capacitance model. Activity factors are then estimated by the model and input vectors of the circuit. Input vectors are generated by architecture-level simulations in the form of streaming. For real-time and high-speed power evaluation, a data streaming framework is proposed for massive parallelism. The cross-layer estimation is verified based on the functional units of PULPino processor running SPEC CPU2006 benchmarks. Compared with the SPICE results using SMIC 28-nm PDK, our cycle-by-cycle dynamic power analysis shows an average error of 5.4%. Meanwhile, our approach realizes 65.2% faster than the traditional PTPX simulation and 48.8% faster compared with the state-of-art cross-level evaluation method.