OpenCGRA: Democratizing Coarse-Grained Reconfigurable Arrays
Cheng Tan, Nícolas Bohm Agostini, Jeff Zhang, Marco Minutoli, Vito Giovanni Castellana, Chenhao Xie, Tong Geng, Ang Li, Kevin Barker, Antonino Tumeo
Abstract
Reconfigurable architectures are today experiencing a renewed interest for their ability to provide specialization without sacrificing the capability to adapt to disparate workloads. Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) while offering increased hardware efficiency with respect to field-programmable gate arrays (FPGAs). This makes CGRAs a promising alternative to enable power-/area-efficient acceleration across different application domains. Unfortunately, specializing and implementing a CGRA for a specific application domain requires the exploration in a large design space (e.g., applying appropriate loop transformation on each application, specializing the reconfigurable processing elements of the CGRA, refining the network topology, deciding the size of the data memory, etc.) and involves enormous software/hardware engineering effort (e.g., modeling, testing, and evaluating the CGRA, map operations onto the CGRA, etc). In this paper, we discuss a hardware/software co-design framework<sup>*</sup> to automatically specialize and implement optimal CGRA designs given a set of applications of interest.