Achieving Energy Efficiency for Near-Threshold Circuits Through Postfabrication Calibration and Adaptation
| Autor: | Fabian Oboril, Mohammad Saber Golanbari, Saman Kiamehr, Mehdi B. Tahoori, Anteneh Gebregiorgis |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Circuit design 02 engineering and technology Energy consumption 020202 computer hardware & architecture Power (physics) Hardware and Architecture Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Electronic engineering Sensitivity (control systems) Electrical and Electronic Engineering Software Energy (signal processing) Efficient energy use Voltage Electronic circuit |
| Zdroj: | IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:443-455 |
| ISSN: | 1557-9999 1063-8210 |
| Popis: | Scaling supply voltage to the near-threshold voltage (NTV) region is an effective approach for energy-constrained circuit design at the cost of acceptable performance reduction. However, by operating in the NTV region, the sensitivity of circuits to process and runtime variations significantly aggravates. Therefore, the performance and power consumption of a circuit is largely impacted by the variabilities, which affects the operating voltage for the most efficient computation, i.e., the minimum energy point (MEP). Accordingly, finding an optimum operating voltage for near-threshold computing (NTC) to account for variabilities is very challenging. In this article, we propose an MEP calibration and adaptation approach based on machine learning to tune for minimal energy operation on a per-chip basis by considering process and runtime variations. In the proposed approach, the optimal supply voltage of each chip is determined during manufacturing tests by characterizing dynamic and leakage power and at runtime by considering the impact of temperature variation. The presented method does not require costly power measurement circuitry on chip. The simulation results show that the proposed method has high MEP prediction accuracy and achieves near-optimal operation by only 1.2% higher energy consumption compared with the optimal operation. |
| Databáze: | OpenAIRE |
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