Convolutional Neural Network (CNN) has attained high accuracy and it has been widely employed in image recognition tasks. In recent times, deep learning-based modern applications are evolving and it poses a challenge in research and development of hardware implementation. Therefore, hardware optimization for efficient accelerator design of CNN remains a challenging task. A key component of the accelerator design is a processing element (PE) that implements the convolution operation. To reduce the amount of hardware resources and power consumption, this article provides a new processing element design as an alternate solution for hardware implementation. Modified BOOTH encoding (MBE) multiplier and WALLACE tree-based adders are proposed to replace bulky MAC units and typical adder tree respectively. The proposed CNN accelerator design is tested on Zynq-706 FPGA board which achieves a throughput of 87.03 GOP/s for Tiny-YOLO-v2 architecture. The proposed design allows to reduce hardware costs by 24.5% achieving a power efficiency of 61.64 GOP/s/W that outperforms the previous designs.
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Power Efficient Tiny Yolo CNN Using Reduced Hardware Resources Based on Booth Multiplier and WALLACE Tree Adders
