Cascade: An Application Pipelining Toolkit for Coarse-Grained Reconfigurable Arrays

While coarse-grained reconfigurable arrays (CGRAs) have emerged as promising programmable accelerator architectures, they require automatic pipelining of applications during their compilation flow to achieve high performance. Current CGRA compilers either lack pipelining altogether resulting in low application performance, or perform exhaustive pipelining resulting in high power and resource consumption. We address these challenges by proposing Cascade, an end-to-end open-source application compiler for CGRAs that achieves both state-of-the-art performance and fast compilation times. The contributions of this work are: 1) a novel post place-and-route (PnR) application pipelining technique for CGRAs that accounts for interconnect hop delays during pipelining but in a unique way that avoids cyclic scheduling and PnR, 2) a register resource usage optimization technique that leverages the scheduling logic in CGRA memory tiles to minimize the number of register resources used during pipelining, and 3) an automated CGRA timing model generator, an application timing analysis tool, and a large set of existing and novel application pipelining techniques integrated into an end-to-end compilation flow. Cascade achieves 8 - $34\times $ lower critical path delay and 7 - $190\times $ lower energy-delay product (EDP) across a variety of dense image processing and machine learning workloads, and 3 - $5.2\times $ lower critical path delay and 2.5 - $5.2\times $ lower EDP on sparse workloads, compared to a compiler without pipelining. Cascade mitigates the performance and energy-efficiency drawbacks of existing CGRA compilers, and enables further research into CGRAs as flexible, yet competitive accelerator architectures.