Automatic source code specialization for energy reduction
Eui-Young Chung, Luca Benini, Giovanni De Micheli · 2001
This paper presents a framework to reduce the computational eort of software programs, using value pro ling and partial evaluation. Our tool reduces computational eort by specializing a program for highly expected situations and such a reduction translates into both energy and performance improvement. Procedure calls executed frequently with same parameter values are de ned as highly expected situations (common cases). The choice of the best transformation of common cases is achieved by solving three search problems. The rst identi es eective common cases to be specialized, the second searches for an optimal solution for eective common case, and the third examines the interplay among the specialized cases. Our technique improves both energy consumption and performance of the source code up to more than twice and in average about 25% over the original program. Also, our pruning techniques reduce the searching time by 80% compared to exhaustive approach.