ReAAP: A Reconfigurable and Algorithm-Oriented Array Processor With Compiler-Architecture Co-Design

Jianwei Zheng, Yu Liu, Xuejiao Liu, Luhong Liang, Deming Chen, Kwang Ting Cheng

Research output: Contribution to journalArticlepeer-review


Parallelism and data reuse are the most critical issues for the design of hardware acceleration in a deep learning processor. Besides, abundant on-chip memories and precise data management are intrinsic design requirements because most of deep learning algorithms are data-driven and memory-bound. In this paper, we propose a compiler-architecture co-design scheme targeting a reconfigurable and algorithm-oriented array processor, named ReAAP. Given specific deep neural networks, the proposed co-design scheme is effective to perform parallelism and data reuse optimization on compute-intensive layers for guiding reconfigurable computing in hardware. Especially, the systemic optimization is performed in our proposed domain-specific compiler to deal with the intrinsic tensions between parallelism and data locality, for the purpose of automatically mapping diverse layer-level workloads onto our proposed reconfigurable array architecture. In this architecture, abundant on-chip memories are software-controlled and its massive data access is precisely handled by compiler-generated instructions. In our experiments, the ReAAP is implemented on an embedded FPGA platform. Experimental results demonstrate that our proposed co-design scheme is effective to integrate software flexibility with hardware parallelism for accelerating diverse deep learning workloads. As a whole system, ReAAP achieves a consistently high utilization of hardware resource for accelerating all the diverse compute-intensive layers in ResNet, MobileNet, and BERT.

Original languageEnglish (US)
Pages (from-to)3088-3100
Number of pages13
JournalIEEE Transactions on Computers
Issue number12
StatePublished - Dec 1 2022


  • Domain-specific processor
  • compiler-architecture co-design
  • diverse layer-level workloads
  • polyhedral modeling
  • reconfigurable computing

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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