Active power-gating-induced power/ground noise alleviation using parasitic capacitance of on-chip memories

Authors:
Xuan Wang;Jiang Xu;Wei Zhang;Xiaowen Wu;Yaoyao Ye;Zhehui Wang;Mahdi Nikdast;Zhe Wang
Affiliations:
The Hong Kong University of Science and Technology;The Hong Kong University of Science and Technology;Nanyang Technological University, Singapore;The Hong Kong University of Science and Technology;The Hong Kong University of Science and Technology;The Hong Kong University of Science and Technology;The Hong Kong University of Science and Technology;The Hong Kong University of Science and Technology
Venue:
Proceedings of the Conference on Design, Automation and Test in Europe
Year:
2013

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Abstract

By integrating multiple processing units and memories on a single chip, multiprocessor system-on-chip (MPSoC) can provide higher performance per energy and lower cost per function to applications with growing complexity. In order to maintain the power budget, power gating technique is widely used to reduce the leakage power. However, it will introduce significant power/ground (P/G) noises, and threat the reliability of MPSoCs. With significant area, power and performance overheads, traditional methods rely on reinforced circuits or fixed protection strategies to reduce P/G noises caused by power gating. In this paper, we propose a systematic approach to actively alleviating P/G noises using the parasitic capacitance of on-chip memories through sensor network on-chip (SENoC). We utilize the parasitic capacitance of on-chip memories as dynamic decoupling capacitance to suppress P/G noises and develop a detailed Hspice model for related study. SENoC is developed to not only monitor and report P/G noises but also coordinate processing units and memories to alleviate such transient threats at run time. Extensive evaluations show that compared with traditional methods, our approach saves 11.7% to 62.2% energy consumption and achieves 13.3% to 69.3% performance improvement for different applications and MPSoCs with different scales. We implement the circuit details of our approach and show its low area and energy consumption overheads.