Answer Set versus Integer Linear Programming for Automatic Synthesis of Multiprocessor Systems from Real-Time Parallel Programs

An automated design approach for multiprocessor systems on FPGAs is presented which customizes architectures for parallel programs by simultaneously solving the problems of task mapping, resource allocation, and scheduling. The latter considers effects of fixed-priority preemptive scheduling in order to guarantee real-time requirements, hence covering a broad spectrum of embedded applications. Being inherently a combinatorial optimization problem, the design space is modeled using linear equations that capture high-level design parameters. A comparison of two methods for solving resulting problem instances is then given. The intent is to study how well recent advances in propositional satisfiability (SAT) and thus Answer Set Programming (ASP) can be exploited to automate the design of flexible multiprocessor systems. Integer Linear Programming (ILP) is taken as a baseline, where architectures for IEEE 802.11g and WCDMA baseband signal processing are synthesized. ASP-based synthesis used a few seconds in the solver, faster by three orders of magnitude compared to ILP-based synthesis, thereby showing a great potential for solving difficult instances of the automated synthesis problem