Technology Trends and Adaptive Computing

  • Michael J. Flynn
  • Albert A. Liddicoat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2147)


System and processor architectures depend on changes in technology. Looking ahead as die density and speed increase, power consumption and on chip interconnection delay become increasingly important in defining architecture tradeoffs. While technology improvements enable increasingly complex processor implementations, there are physical and program behavior limits to the usefulness of this complexity at the processor level. The architecture emphasis then shifts to the system: integrating controllers, signal processors, and other components with the processor to achieve enhanced system performance. In dealing with these elements, adaptability or reconfiguration is essential for optimizing system performance in a changing application environment. A hierarchy of adaptation is proposed based on flexible processor architectures, traditional FPL, and a new coarse grain adaptive arithmetic cell. The adaptive arithmetic cell offers high-performance arithmetic operations while providing computational flexibility. The proposed cell offers efficient and dynamic reconfiguration of the arithmetic units. Hybrid fine and coarse grain techniques may offer the best path to the continued evolution of the processor-based system.


Arithmetic Operation Technology Trend Instruction Level Parallelism Memory Access Time Adaptive Computing 
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 2001

Authors and Affiliations

  • Michael J. Flynn
    • 1
  • Albert A. Liddicoat
    • 1
  1. 1.Computer Systems LaboratoryDepartment of Electrical Engineering Stanford UniversityStanfordUSA

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