XNORCONV: CNNs accelerator implemented on FPGA using a hybrid CNNs structure and an inter‐layer pipeline method. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Zhang, Lin; Bu, Xiaokang; Li, Bing
• Is Part Of: IET image processing. Volume 14:Issue 1(2020)
• Page Start: 105
• Page End: 113
• Publication Date: 2020-01-01
• Subjects: field programmable gate arrays -- logic gates -- convolutional neural nets -- neural chips -- multiplying circuits -- computational complexity -- pipeline processing -- logic design; XNORCONV -- CNNs accelerator -- FPGA -- hybrid CNNs structure -- convolutional neural networks -- computer vision -- pattern recognition -- energy consumption -- graphic processing units‐based CNNs -- field‐programmable gate array -- convolutional layer -- XNOR operations -- multiplier -- computational complexity -- inter‐layer pipeline design -- Xilinx Zynq‐7000 xc7z020clg400‐1 -- clock frequency -- MNIST dataset; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0385
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Nowadays, convolutional neural networks (CNNs) have become a research hotspot because of their high performance in computer vision and pattern recognition. However, as the high energy consumption of traditional graphic processing units‐based CNNs, it is difficult to deploy them into portable devices. To deal with this problem, a hybrid CNN structure (XNORCONV) was proposed and implemented on field‐programmable gate array (FPGA) in this study. Two improvements have been applied in XNORCONV. Firstly, the multiplications in the convolutional layer (CONV) were replaced by XNOR operations to save the multiplier and reduce computational complexity. Secondly, an inter‐layer pipeline was designed to further accelerate the calculation. XNORCONV was implemented on Xilinx Zynq‐7000 xc7z020clg400‐1 under the clock frequency of 150 MHz and tested with MNIST dataset. The results of the experiment show that XNORCONV can classify each picture from MNIST in 18.97 μ s, and achieve 98.4% recognition accuracy. Compared with traditional Lenet‐5 on different platforms, XNORCONV reduced multiplication by 85.6% with only 0.4% accuracy loss.