Provided by: Imperial College London
Date Added: Jun 2008
A geometric programming framework is proposed in this paper to automate exploration of the design space consisting of data reuse (buffering) exploitation and loop-level parallelization, in the paper of FPGA-targeted hardware compilation. The authors expose the dependence between data reuse and data-level parallelization and explore both problems under the on-chip memory constraint for performance-optimal designs within a single optimization step. Results from applying this framework to several real benchmarks demonstrate that given different constraints on on-chip memory utilization, the corresponding performance-optimal designs are automatically determined by the framework, and performance improvements up to 4.7 times have been achieved compared with the method that first explores data reuse and then performs parallelization.