• DocumentCode
    3403418
  • Title

    Power aware transformation of bandlimited signals

  • Author

    Krishnamoorthy, P. ; Tekumalla, Ramesh

  • Author_Institution
    APAC SOC Design, LSI Corp., Allentown, PA, USA
  • fYear
    2013
  • fDate
    4-6 Sept. 2013
  • Firstpage
    178
  • Lastpage
    183
  • Abstract
    This work presents an universal, lossless, linear, low complexity data transformation algorithm based on the sampling theorem that significantly reduces the complexity of operations in data processing systems. While many lossy and lossless compression methods are in existence, the data compressed by such methods cannot be directly processed by signal processing systems without decompression due to the non linear nature of most data compression algorithms. If there were linear compression method in existence, then compressed data could directly be passed to digital signal processing systems. Doing so significantly reduces the complexity of signal processing operations including additional benefits in the form of reduced power dissipation, improved signal integrity and better bandwidth utilization. This approach can be used in applications for the storage and retrieval of data from storage mediums such as read channels, system and processor interconnects including memory buses. System level simulations show that this method not only reduces the amount of data, but also improves performance due to improved signal integrity and significant reduction in power dissipation (upto 15% reduction) associated with the processing of multimedia content.
  • Keywords
    bandlimited signals; data compression; power aware computing; signal sampling; bandlimited signal; data compression algorithm; data processing system; data retrieval; data storage; linear complexity data transformation algorithm; linear compression method; lossless compression method; low complexity data transformation algorithm; multimedia content; power aware transformation; power dissipation; sampling theorem; signal integrity improvement; signal processing; Adders; Complexity theory; Data compression; Decoding; Power dissipation; Signal processing; Transmitters;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SOC Conference (SOCC), 2013 IEEE 26th International
  • Conference_Location
    Erlangen
  • ISSN
    2164-1676
  • Type

    conf

  • DOI
    10.1109/SOCC.2013.6749684
  • Filename
    6749684