Design Tools for Application Specific Embedded Processors

  • Wei Qin
  • Subramanian Rajagopalan
  • Manish Vachharajani
  • Hangsheng Wang
  • Xinping Zhu
  • David August
  • Kurt Keutzer
  • Sharad Malik
  • Li-Shiuan Peh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2491)


A variety of factors make it increasingly difficult and expensive to design and manufacture traditional Application Specific Integrated Circuits (ASICs). Consequently, programmable alternatives are more attractive than ever. The flexibility provided by programmability comes with a performance and power overhead. This can be significantly mitigated by using application specific platforms, also referred to as Application Specific Embedded Processors, or Application Specific Instruction Set Processors (ASIPs).

ASIPs and the embedded software applications running on them, require specialized design tools - both during architectural evaluation to provide feedback on the suitability of the architecture for the application; as well as during system implementation to ensure efficient mapping and validation of design constraints. These functions result in requirements different from those of traditional software development environments. The first requirement is retargetability, especially during the early architectural evaluation stage where a rapid examination of design alternatives is essential. The second requirement is for additional metrics such as power consumption, real-time constraints and code size.

This paper describes a set of design tools and associated methodology designed to meet the challenges posed by architectural evaluation and software synthesis. This work is part of the MESCAL (Modern Embedded Systems, Compilers, Architectures, and Languages) project1.


Pipeline Stage Attribute Grammar Instruction Level Parallelism Compatibility Graph Branch Predictor 
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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Wei Qin
    • 1
  • Subramanian Rajagopalan
    • 1
  • Manish Vachharajani
    • 1
  • Hangsheng Wang
    • 1
  • Xinping Zhu
    • 1
  • David August
    • 1
  • Kurt Keutzer
    • 2
  • Sharad Malik
    • 1
  • Li-Shiuan Peh
    • 1
  1. 1.Princeton UniversityPrincetonUSA
  2. 2.UC BerkeleyBerkeleyUSA

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