A Framework for Algorithmic and Architectural Transformations

  • Manesh Mehendale
  • Sunil D. Sherlekar


Chapters 2 to 7 have presented many algorithmic and architectural transformations targeted to the weighted-sum and the MCM computations realized using different implementation styles. This chapter proposes a framework that encapsulates these transformations and enables a systematic exploration of the area-delay-power solution space. The framework is based on a classification of the transformations into seven categories which exploit unique properties of the DSP algorithms and the implementation styles.


Power Dissipation Gray Code Data Flow Graph Implementation Style Start Address 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Manesh Mehendale
    • 1
  • Sunil D. Sherlekar
    • 2
  1. 1.Texas Instruments (India), Ltd.USA
  2. 2.Silicon Automation Systems Ltd.USA

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