X-CGRA: An Energy-Efficient Approximate Coarse-Grained Reconfigurable Architecture

摘要

In this article, we present an energy-efficient approximate CGRA (X-CGRA). Instead of conventional exact arithmetic units, it employs configurable approximate adders and multipliers in the so-called quality-scalable processing elements (QSPEs). Furthermore, the structure and functionality of the other architectural components, like context memory, are modified based on the quality-scalable operating modes of the QSPEs. The quality reconfigurability of the X-CGRA makes it amenable for both error-resilient and nonresilient applications. To map the applications on the X-CGRA, a mapping technique is proposed that efficiently utilizes the QSPEs and selects appropriate approximation modes in order to lower the energy consumption while satisfying a user-defined quality constraint. We evaluate the efficacy of our X-CGRA for several benchmark applications from different domains, including image/video processing, signal processing, and scientific computations. Different sizes of X-CGRA are synthesized using a 15-nm FinFET technology. For these benchmarks, the results indicate energy consumption reduction of up to 3.21x compared to those of a typical exact CGRA, at the cost of 4% quality loss.