Adaptive Approximate Computing with CGRAgen

Abstract

Modern Edge applications are diverse and compute-intensive but can often afford constrained computational inaccuracy. Commonly denoted approximate computing, this paper explores the integration of inexact arithmetic units into Coarse-Grained Reconfigurable Array (CGRA) architectures with an associated, comprehensive framework for architecture modeling, hardware generation, and automated approximation, CGRAgen. A use case kernel from signal processing illustrates the functionality and potential of CGRAgen in design space explorations into CGRAs with integrated approximate computing techniques, highlighting the utility of making approximations reconfigurable.