Universität Karlsruhe
Fuzzy-Neuro-Controlled Verified Instruction Scheduler
@conference{,
  author={Thilo Gaul and Martin Riedmiller and Ralf Schoknecht and Martin Spott},
  title=\{Fuzzy-Neuro-Controlled Verified Instruction Scheduler},
  booktitle=\{Proc. of NAFIPS99},
  year=\{1999},
  month=\{Jun},
  abstract=\{In this paper we present a fuzzy-neuro approach for instruction   scheduling in compilers for modern high-performance processors.   Instruction Scheduling is an optimization problem in NP and is   usually addressed with processor dependent heuristics or processor   simplifying cost models. The costs for executing a given instruction   sequence on the processor can not be determined exactly in practice,   because the exact execution model is too complex or simply not   available from the manufacturer. Our approach enables the compiler   to adapt the cost measure dynamically by learning the processor   behavior and typical optimization situations on the basis of   reinforcement learning.  Additionally we are able to include fuzzy a   priori scheduling knowledge and derive verified implementations by   the technique of program-checking. },
}

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