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

Omid Akbari, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review


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 3.21× compared to those of a typical exact CGRA, at the cost of 4% quality loss.

Original languageEnglish (US)
Article number8815818
Pages (from-to)2558-2571
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number10
StatePublished - Oct 2020


  • Adaptivity
  • approximate computing
  • coarsegrained reconfigurable architecture (CGRA)
  • energy efficiency
  • error resilience
  • mapping
  • quality of service (QoS)

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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