Mobileware: Distributed Architecture With Channel Stationary Dataflow for MobileNet Acceleration

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초록

The depthwise separable convolution, a key feature of the MobileNet models, has a different input reuse pattern from the conventional standard convolution, and a smaller number of input/weight pairs are used for a dot product, thereby leading to extremely low MAC utilization. This article proposes a Mobileware architecture for the high-performance acceleration of the MobileNet workloads. A new channel stationary dataflow architecture distributes the on-chip buffers, and the distributed SRAMs are placed near each processing element (PE). By doing so, PEs and SRAMs can communicate with high bandwidth. Our Mobileware architecture shows 1.4 x- 29.5 x higher throughput than conventional weight stationary-based hardware architecture, and the proposed design was verified on the Xilinx ZCU102 FPGA evaluation board.

키워드

ConvolutionRandom access memoryStandardsComputer architectureDesign automationSystem-on-chipComputational modelingDataflowdeep neural networkdepthwise convolutiondistributed memoryhardware acceleratorMobileNetprocessing element (PE)
제목
Mobileware: Distributed Architecture With Channel Stationary Dataflow for MobileNet Acceleration
저자
Ryu, SungjuJang, JaeyongOh, YoungtaekKim, Jae-Joon
DOI
10.1109/TCAD.2024.3380555
발행일
2024-09
유형
Article
저널명
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
43
9
페이지
2661 ~ 2673