Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Predictive system shutdown and other architectural techniques for energy efficient programmable computation

Published: 01 March 1996 Publication History

Abstract

With the popularity of portable devices such as personal digital assistants and personal communicators, as well as with increasing awareness of the economic and environmental costs of power consumption by desktop computers, energy efficiency has emerged as an important issue in the design of electronic systems. While power efficient ASIC's with dedicated architectures have addressed the energy efficiency issue for niche applications such as DSP, much of the computation continues to be implemented as software running on programmable processors such as microprocessors, microcontrollers, and programmable DSP's. Not only is this true for general purpose computation on personal computers and workstations, but also for portable devices, application-specific systems etc. In fact, firmware and embedded software executing on RISC and DSP processor cores that are embedded in ASIC's has emerged as a leading implementation methodology for speech coding, modem functionality, video compression, communication protocol processing etc. This paper describes architectural techniques for energy efficient implementation of programmable computation, particularly focussing on the computation needed in portable devices where event-driven user interfaces, communication protocols, and signal processing play a dominant role. Two key approaches described here are predictive system shutdown and extended voltage scaling. Results indicate that a large reduction in power consumption can be achieved over current day solutions with little or no loss in system performance.

Cited By

View all
  • (2020)Clustering-Based Scenario-Aware LTE Grant Prediction2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120789(1-7)Online publication date: 25-May-2020
  • (2018)Reinforcement Learning for Power-Efficient Grant Prediction in LTEProceedings of the 21st International Workshop on Software and Compilers for Embedded Systems10.1145/3207719.3207722(18-26)Online publication date: 28-May-2018
  • (2017)Exploiting Predictability in Dynamic Network Communication for Power-Efficient Data Transmission in LTE Radio SystemsProceedings of the 20th International Workshop on Software and Compilers for Embedded Systems10.1145/3078659.3078670(64-67)Online publication date: 12-Jun-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems  Volume 4, Issue 1
March 1996
133 pages
ISSN:1063-8210
Issue’s Table of Contents

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 March 1996

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 04 Oct 2024

Other Metrics

Citations

Cited By

View all
  • (2020)Clustering-Based Scenario-Aware LTE Grant Prediction2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120789(1-7)Online publication date: 25-May-2020
  • (2018)Reinforcement Learning for Power-Efficient Grant Prediction in LTEProceedings of the 21st International Workshop on Software and Compilers for Embedded Systems10.1145/3207719.3207722(18-26)Online publication date: 28-May-2018
  • (2017)Exploiting Predictability in Dynamic Network Communication for Power-Efficient Data Transmission in LTE Radio SystemsProceedings of the 20th International Workshop on Software and Compilers for Embedded Systems10.1145/3078659.3078670(64-67)Online publication date: 12-Jun-2017
  • (2017)Stopping-Time Management of Smart Sensing Nodes Based on Tradeoffs Between Accuracy and Power ConsumptionIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2017.271274825:9(2472-2485)Online publication date: 1-Sep-2017
  • (2017)Time and Energy Optimization Algorithms for the Static Scheduling of Multiple Workflows in Heterogeneous Computing SystemJournal of Grid Computing10.1007/s10723-017-9391-515:4(435-456)Online publication date: 1-Dec-2017
  • (2016)Hybrid Power Management for Office EquipmentACM Transactions on Design Automation of Electronic Systems10.1145/291058222:1(1-22)Online publication date: 23-Nov-2016
  • (2016)A New Optimal Algorithm for Energy Saving in Embedded System With Multiple Sleep ModesIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2015.241482724:2(706-719)Online publication date: 1-Feb-2016
  • (2016)Energy and time constrained task scheduling on multiprocessor computers with discrete speed levelsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.02.00695:C(15-28)Online publication date: 1-Sep-2016
  • (2016)Minimum energy consumption for rate monotonic scheduled tasksComputing10.1007/s00607-015-0475-498:6(661-684)Online publication date: 1-Jun-2016
  • (2015)Bayesian Prediction-Based Energy-Saving Algorithm for Embedded Intelligent TerminalIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2014.238579123:12(2902-2912)Online publication date: 1-Dec-2015
  • Show More Cited By

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media