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Convolutional neural network (CNN) has been widely adopted in many tasks. Its inference process is usually applied on edge devices where the computing resources and power consumption are limited. At present, the performance of general processors cannot meet the requirement for CNN models with high computation complexity and large number of parameters. Field-programmable gate array (FPGA)-based custom computing architecture is a promising solution to further enhance the CNN inferencedoi:10.1049/cje.2020.11.002 fatcat:vt4n4x67k5g6bhkywe7rhm7tda