A High-Throughput Full-Dataflow MobileNetv2 Accelerator on Edge FPGA

Weixiong Jiang, Heng Yu, Yajun Ha

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)


FPGA accelerators for lightweight neural networks, such as MobileNetv2, are of great need in edge computing applications with high throughput requirements. Dataflow architecture has been considered a promising approach to optimize throughput since the intermediate feature map transfers can be significantly saved. However, previous MobileNetv2 accelerators only achieved a partial-dataflow architecture, and just one-third of the feature map transfers can be saved. To solve this issue, we propose a scheme to achieve a full-dataflow MobileNetv2 accelerator on FPGA. The scheme contains four techniques. First, we improve the full-integer quantization for easier deployment on hardware. Second, we propose tunable activation weight imbalance transfer for less quantization accuracy loss. Third, we present several highly optimized accelerator components whose parallelism can be flexibly adjusted and implement residual connection with deeper FIFO so that the requirements of the full-dataflow architecture can be fully met. Finally, we present a computing resource allocation strategy to balance the latency of each layer, and a memory resource allocation strategy to effectively use the on-chip memory. Compared to the state-of-the-art, experimental results show that the accelerator achieves 1910 FPS with 1.8× speedup when implemented on the Xilinx ZCU102 FPGA. In addition, it reaches 72.98% Top-1 accuracy with 8-bit integer quantization that outperforms all the other MobileNetv2 accelerators.

Original languageEnglish
Pages (from-to)1532-1545
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number5
Publication statusPublished - 1 May 2023


  • Convolution neural network (CNN)
  • FPGA accelerator
  • MobileNetv2

ASJC Scopus subject areas

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


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