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ReAAP: A Reconfigurable and Algorithm-Oriented Array Processor With Compiler-Architecture Co-Design.
- Source :
- IEEE Transactions on Computers; Dec2022, Vol. 71 Issue 12, p3088-3100, 13p
- Publication Year :
- 2022
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189340
- Volume :
- 71
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Computers
- Publication Type :
- Academic Journal
- Accession number :
- 160620905
- Full Text :
- https://doi.org/10.1109/TC.2022.3213177