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Statistical Optimization of FinFET Processor Architectures under PVT Variations Using Dual Device-Type Assignment

Statistical Optimization of FinFET Processor Architectures under PVT Variations Using Dual... With semiconductor technology scaling to the 22nm node and beyond, fin field-effect transistor (FinFET) has started replacing complementary metal-oxide semiconductor (CMOS), thanks to its superior control of short-channel effects and much lower leakage current. However, process, supply voltage, and temperature (PVT) variations across the integrated circuit (IC) become worse with technology scaling. Thus, to analyze timing, leakage power, and dynamic power under PVT variations, statistical analysis/optimization techniques are more suitable than traditional static timing/power analysis and optimization counterparts. In this article, we propose a statistical optimization framework using dual device-type assignment at the architecture level under PVT variations that takes spatial correlations into account and leverages circuit-level statistical analysis techniques. To the best of our knowledge, this is the first work to study statistical optimization at the system level under PVT variations. Simulation results show that leakage power yield and dynamic power yield at the mean value of the baseline can be improved by up to 44.2% and 21.2%, respectively, with no loss in timing yield for a single-core processor and up to 43.0% and 50.0%, respectively, without any loss in timing yield for an 8-core chip multiprocessor (CMP), at little area overhead. Under the same (99.0%) power yield constraints, leakage power and dynamic power are reduced by up to 91.2% and 4.3%, respectively, for a single-core processor, and up to 44.6% and 12.5%, respectively, for an 8-core CMP, with no loss in timing yield. We also show that optimizations performed without taking module-to-module and core-to-core spatial correlations into account overestimate yield, establishing the importance of taking such correlations into account. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Journal on Emerging Technologies in Computing Systems (JETC) Association for Computing Machinery

Statistical Optimization of FinFET Processor Architectures under PVT Variations Using Dual Device-Type Assignment

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 ACM
ISSN
1550-4832
eISSN
1550-4840
DOI
10.1145/3110714
Publisher site
See Article on Publisher Site

Abstract

With semiconductor technology scaling to the 22nm node and beyond, fin field-effect transistor (FinFET) has started replacing complementary metal-oxide semiconductor (CMOS), thanks to its superior control of short-channel effects and much lower leakage current. However, process, supply voltage, and temperature (PVT) variations across the integrated circuit (IC) become worse with technology scaling. Thus, to analyze timing, leakage power, and dynamic power under PVT variations, statistical analysis/optimization techniques are more suitable than traditional static timing/power analysis and optimization counterparts. In this article, we propose a statistical optimization framework using dual device-type assignment at the architecture level under PVT variations that takes spatial correlations into account and leverages circuit-level statistical analysis techniques. To the best of our knowledge, this is the first work to study statistical optimization at the system level under PVT variations. Simulation results show that leakage power yield and dynamic power yield at the mean value of the baseline can be improved by up to 44.2% and 21.2%, respectively, with no loss in timing yield for a single-core processor and up to 43.0% and 50.0%, respectively, without any loss in timing yield for an 8-core chip multiprocessor (CMP), at little area overhead. Under the same (99.0%) power yield constraints, leakage power and dynamic power are reduced by up to 91.2% and 4.3%, respectively, for a single-core processor, and up to 44.6% and 12.5%, respectively, for an 8-core CMP, with no loss in timing yield. We also show that optimizations performed without taking module-to-module and core-to-core spatial correlations into account overestimate yield, establishing the importance of taking such correlations into account.

Journal

ACM Journal on Emerging Technologies in Computing Systems (JETC) – Association for Computing Machinery

Published: Sep 21, 2017

Keywords: Chip multiprocessor

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