Value-sensitive automatic code specialization for embedded software

The objective of this work is to create a framework for the optimization of embedded software. We present algorithms and a tool flow to reduce the computational effort of programs, using value profiling and partial evaluation. Such a reduction translates into both energy savings and average-case performance improvement, while preserving a tolerable increase of worst case performance and code size. Our tool reduces the computational effort by specializing frequently executed procedures for the most common values of their parameters. The most effective specializations are automatically searched and identified, and the code is transformed through partial evaluation. Experimental results show that their technique improves both energy consumption and performance of the source code up to more than a factor of two, in average about 35% over the original program. Also, their automatic search engine greatly reduces code optimization time with respect to exhaustive search