R-Accelerator: An RRAM-Based CGRA Accelerator With Logic Contraction

Zhengyu Chen*, Hai Zhou, Jie Gu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this paper, a novel RRAM-based reconfigurable accelerator (R-accelerator) design is proposed, which makes special use of existing RRAM device for high-efficient reconfigurable application-specific computing. The proposed R-accelerator design consists of RRAM-based arithmetic unit (AU) array, fully integrated into commercial EDA design tools. A significant area saving is achieved compared with conventional digital counterpart due to the proposed logic contraction technique, as well as saving of storage space and routing congestions. For enabling the optimization of the AU array, this paper also proposes a systematical method on the synthesis of the AU array under routing channel constraint for application-specific designs. Two automatic mapping algorithms, including simultaneous mapping and incremental mapping algorithms, are proposed and compared. The experiments using 45-nm CMOS technology on a case study of dynamic time warping example and general benchmark programs show up to 49% area reduction and 28% performance enhancement using the proposed R-accelerator technique compared with conventional application-specified integrated circuit (ASIC) design.

Original languageEnglish (US)
Article number8782148
Pages (from-to)2655-2667
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number11
StatePublished - Nov 2019


  • Coarse grain reconfigurable array (CGRA)
  • RRAM
  • logic contraction
  • reconfigurable accelerator (R-accelerator)

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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