Wattch

Author:

Brooks David1,Tiwari Vivek2,Martonosi Margaret1

Affiliation:

1. Department of Electrical Engineering, Princeton University

2. Intel Corporation

Abstract

Power dissipation and thermal issues are increasingly significant in modern processors. As a result, it is crucial that power/performance tradeoffs be made more visible to chip architects and even compiler writers, in addition to circuit designers. Most existing power analysis tools achieve high accuracy by calculating power estimates for designs only after layout or floorplanning are complete. In addition to being available only late in the design process, such tools are often quite slow, which compounds the difficulty of running them for a large space of design possibilities. This paper presents Wattch, a framework for analyzing and optimizing microprocessor power dissipation at the architecture-level. Wattch is 1000X or more faster than existing layout-level power tools, and yet maintains accuracy within 10% of their estimates as verified using industry tools on leading-edge designs. This paper presents several validations of Wattch's accuracy. In addition, we present three examples that demonstrate how architects or compiler writers might use Wattch to evaluate power consumption in their design process. We see Wattch as a complement to existing lower-level tools; it allows architects to explore and cull the design space early on, using faster, higher-level tools. It also opens up the field of power-efficient computing to a wider range of researchers by providing a power evaluation methodology within the portable and familiar SimpleScalar framework.

Publisher

Association for Computing Machinery (ACM)

Cited by 154 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep-Learning-Based Adaptive Error-Correction Decoding for Spin-Torque Transfer Magnetic Random Access Memory (STT-MRAM);IEEE Transactions on Magnetics;2023-11

2. PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions;2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD);2023-10-28

3. A Novel Integrated Simulation Framework for Cyber-Physical Systems Modelling;IEEE Transactions on Parallel and Distributed Systems;2023-10

4. A Survey on Run-time Power Monitors at the Edge;ACM Computing Surveys;2023-07-17

5. CAMJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-Sensor Visual Computing;Proceedings of the 50th Annual International Symposium on Computer Architecture;2023-06-17

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