A New Dataflow Compiler IR for Accelerating Control-Intensive Code in Spatial Hardware

Abstract

While custom (and reconfigurable) computing can provide orders-of-magnitude improvements in energy efficiency and performance for many numeric, data-parallel applications, performance on non-numeric, sequential code is often worse than what is achievable using conventional superscalar processors. This work attempts to address the problem of improving sequential performance in custom hardware by (a) switching from a statically scheduled to a dynamically scheduled (dataflow) execution model, and (b) developing a new compiler IR for high-level synthesis that enables aggressive exposition of ILP even in the presence of complex control flow. This new IR is directly implemented as a static dataflow graph in hardware by our prototype high-level synthesis tool-chain, and shows an average speedup of 1.13× over equivalent hardware generated using LegUp, an existing HLS tool. In addition, our new IR allows us to further trade area and energy for performance, increasing the average speedup to 1.55×, through loop unrolling, with a peak speedup of 4.05×. Our custom hardware is able to approach the sequential cycle counts of an Intel Nehalem Core i7 superscalar processor, while consuming on average only 0.25× the energy of an in-order Altera Nios IIf processor.