Throughput-memory footprint trade-off in synthesis of streaming software on embedded multiprocessors

We study the trade-off between throughput and memory footprint of embedded software that is synthesized from acyclic static dataflow (task graph) specifications targeting distributed memory multiprocessors. We identify iteration overlapping as a knob in the synthesis process by which one can trade application throughput for its memory requirement. Given an initial processor assignment and non-overlapped task schedule, we formally present underlying properties of the problem, such as constraints on a valid iteration overlapping, maximum possible throughput, and minimum memory footprint. Moreover, we develop an effective algorithm for generation of a rich set of design points that provide a range of trade-off options. Experimental results on a number of applications and architectures validate the effectiveness of our approach.