skip to main content
10.1145/2591513.2591517acmconferencesArticle/Chapter ViewAbstractPublication PagesglsvlsiConference Proceedingsconference-collections
research-article

A generic implementation of a quantified predictor on FPGAs

Published:20 May 2014Publication History

ABSTRACT

Predictors are used in many fields of computer architectures to enhance performance. With good estimations of future system behaviour, policies can be developed to improve system performance or reduce power consumption. These policies become more effective if the predictors are implemented in hardware and can provide quantified forecasts and not only binary ones. In this paper, we present and evaluate a generic predictor implemented in VHDL running on an FPGA which produces quantified forecasts. Moreover, a complete scalability analysis is presented which shows that our implementation has a maximum device utilization of less than 5%. Furthermore, we analyse the power consumption of the predictor running on an FPGA. Additionally, we show that this implementation can be clocked by over 210 MHz. Finally, we evaluate a power-saving policy based on our hardware predictor. Based on predicted idle periods, this power-saving policy uses power-saving modes and is able to reduce memory power consumption by 14.3%.

References

  1. M. Awasthi, D. W. Nellans, R. Balasubramonian, and A. Davis. Prediction Based DRAM Row-Buffer Management in the Many-Core Era. In 20th International Conference on Parallel Architecture and Compilation Techniques (PACT), Galveston Island, Texas, October 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K. Chandrasekar, B. Akesson, and K. Goossens. Improved Power Modeling of DDR SDRAMs. In 14th Euromicro Conference on Digital System Design (DSD), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. K. Chandrasekar et al. DRAMPower: Open Source DRAM Power & Energy Estimation Tool. www.es.ele.tue.nl/drampower, 2012.Google ScholarGoogle Scholar
  4. B.-S. Chen, Y.-S. Yang, B.-K. Lee, and T.-H. Lee. Fuzzy Adaptive Predictive Flow Control of ATM Network traffic. IEEE Transactions on Fuzzy Systems, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. Deliparaschos, F. Nenedakis, and S. Tzafestas. Design and implementation of a fast digital fuzzy logic controller using fpga technology. Journal of Intelligent and Robotic Systems, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y. Huang, K.-K. Chou, C.-T. King, and S.-Y. Tseng. NTPT: On the End-to-End Traffic Prediction in the On-Chip Networks. In 47th Design Automation Conference (DAC), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Mellit, H. Mekki, A. Messai, and S. Kalogirou. Fpga-based implementation of intelligent predictor for global solar irradiation, part i: Theory and simulation. Expert Systems with Applications, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Mentor Graphics. ModelSim, 11 2013.Google ScholarGoogle Scholar
  9. Micron Technology Inc. DDR3 SDRAM 1Gb Data Sheet, 2006.Google ScholarGoogle Scholar
  10. L. Miller. Division in VHDL, February 2009.Google ScholarGoogle Scholar
  11. U. Y. Ogras and R. Marculescu. Prediction-based Flow Control for Network-on-Chip Traffic. In 43th Design Automation Conference (DAC), New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. B. Akesson, A. Molnos, A. Hansson, J. Ambrose Angelo, and K. Goossens. Composability and Predictability for Independent Application Development, Verification, and Execution. In Multiprocessor System-on-Chip | Hardware Design and Tool Integration, chapter 2. Springer, 2010.Google ScholarGoogle Scholar
  13. V. Stankovic and N. Milenkovic. DRAM Controller with a Complete Predictor: Preliminary Results. In 7th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services, volume 2, sept. 2005.Google ScholarGoogle ScholarCross RefCross Ref
  14. G. Thomas, K. Chandrasekar, B. Akesson, B. Juurlink, and K. Goossens. A Predictor-Based Power-Saving Policy for DRAM Memories. In 15th Euromicro Conference on Digital System Design (DSD), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. G. Thomas, B. Juurlink, and D. Tutsch. Traffic Prediction for NoCs using Fuzzy Logic. In 2nd International Workshop on New Frontiers in High-performane and Hardware-aware Computing, San Antonio, USA, February 2011. KIT Scientific Publishing.Google ScholarGoogle Scholar
  16. Xilinx. Xilinx Power Estimator 14.3, October 2012.Google ScholarGoogle Scholar
  17. Xilinx. XST User Guide for Virtex-6, Spartan-6, and 7 Series Devices, 14.3 edition, October 2012.Google ScholarGoogle Scholar
  18. Xilinx. ISE Design Suite 14.6, 11 2013.Google ScholarGoogle Scholar
  19. Xilinx. Spartan-6 FPGA Family, 11 2013.Google ScholarGoogle Scholar
  20. Y. Xu, A. S. Agarwal, and B. T. Davis. Prediction in Dynamic SDRAM Controller Policies. In Proc. 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A generic implementation of a quantified predictor on FPGAs

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GLSVLSI '14: Proceedings of the 24th edition of the great lakes symposium on VLSI
      May 2014
      376 pages
      ISBN:9781450328166
      DOI:10.1145/2591513

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 20 May 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      GLSVLSI '14 Paper Acceptance Rate49of179submissions,27%Overall Acceptance Rate312of1,156submissions,27%

      Upcoming Conference

      GLSVLSI '24
      Great Lakes Symposium on VLSI 2024
      June 12 - 14, 2024
      Clearwater , FL , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader