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Optimizing the Power Delivery Network in a Smartphone Platform

Published: 01 January 2014 Publication History

Abstract

Smartphones consume a significant amount of power. Indeed, they can hardly provide a full day of use between charging operations even with a 2000 mAh battery. While power minimization and dynamic power management techniques have been heavily explored to improve the power efficiency of modules (processors, memory, display, GPS, etc.) inside a smartphone platform, there is one critical factor that is often overlooked: the power conversion efficiency of the power delivery network (PDN). This paper focuses on dc-dc converters, which play a pivotal role in the PDN of the smartphone platform. Starting from detailed models of the dc–dc converter designs, two optimization methods are presented: 1) static switch sizing to maximize the efficiency of a dc–dc converter under statistical loading profiles and 2) dynamic switch modulation to achieve the high efficiency enhancement under dynamically varying load conditions. To verify the efficacy of the optimization methods in actual smartphone platforms, this paper also presents a characterization procedure for the PDN. The procedure is as follows: 1) group the modules in the smartphone platform together and use profiling to estimate their average and peak power consumption levels and 2) build an equivalent dc–dc converter model for the power delivery path from the battery source to each group of modules and use linear regression to estimate the conversion efficiency of the corresponding equivalent converter. Experimental results demonstrate that the static switch sizing can achieve 6% power conversion efficiency enhancement, which translates to 19% reduction in power loss general usage of the smartphone. The dynamic switch modulation accomplishes similar improvement at the same condition, while also achieving high efficiency enhancement in various load conditions.

Cited By

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  • (2023)MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile UsersProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596843(247-260)Online publication date: 18-Jun-2023
  • (2020)A Survey of Hierarchical Energy Optimization for Mobile Edge ComputingACM Computing Surveys10.1145/337893553:2(1-44)Online publication date: 17-Apr-2020
  • (2018)Workload-Aware Adaptive Power Delivery System Management for Many-Core ProcessorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277808037:10(2076-2086)Online publication date: 1-Oct-2018
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cover image IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  Volume 33, Issue 1
January 2014
161 pages

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IEEE Press

Publication History

Published: 01 January 2014

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Cited By

View all
  • (2023)MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile UsersProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596843(247-260)Online publication date: 18-Jun-2023
  • (2020)A Survey of Hierarchical Energy Optimization for Mobile Edge ComputingACM Computing Surveys10.1145/337893553:2(1-44)Online publication date: 17-Apr-2020
  • (2018)Workload-Aware Adaptive Power Delivery System Management for Many-Core ProcessorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277808037:10(2076-2086)Online publication date: 1-Oct-2018
  • (2017)Adaptive power delivery system management for many-core processors with on/off-chip voltage regulatorsProceedings of the Conference on Design, Automation & Test in Europe10.5555/3130379.3130677(1265-1268)Online publication date: 27-Mar-2017
  • (2015)Optimizing a Reconfigurable Power Distribution Network in a Multicore PlatformIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2015.239699834:7(1110-1123)Online publication date: 1-Jul-2015
  • (2015)Accurate Predictions of Process-Execution Time and Process Status Based on Support-Vector Regression for Enterprise Information SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2014.238783134:3(354-366)Online publication date: 1-Mar-2015
  • (2014)DR. SwapProceedings of the 2014 international symposium on Low power electronics and design10.1145/2627369.2627647(81-86)Online publication date: 11-Aug-2014

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