Exploration of Trade-offs Between General-Purpose and Specialized Processing Elements in HPC-Oriented CGRA

Coarse-Grained Reconfigurable Arrays (CGRAs) are a class of reconfigurable accelerators traditionally used in embedded computing. Recently, CGRA-like devices have gained traction for HPC and AI acceleration; however, typical HPC and AI workloads often require operations that current CGRAs cannot implement, such as complex mathematical calculations. In this work, we present a broad architectural study exploring potential heterogeneous computational resources in CGRA architectures for HPC, which are not commonly considered in typical CGRA architecture research. We first improved the general-purpose Processing Element (PE) of a baseline CGRA to optimize computational resources and then developed a new specialized PE for mathematical functions commonly found in HPC applications. Finally, we evaluated multiple CGRA configurations concerning floorplan, size, general-purpose/specialized PE ratio, and Power, Performance, and Area (PPA) results from hardware synthesis.