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Deep Convolution Neural Network (CNN) algorithm have recently gained popularity in many applications such as image classification, video analytic, object recognition and segmentation. Being compute-intensive and memory expensive, CNN computations are common accelerated by GPUs with high power dissipations. Recent studies show implementation of CNN on FPGA and it gain higher advantage in term of energy-efficient and flexibility over Software-configurable-GPUs. The proposed framework is verifieddoi:10.11591/ijres.v8.i3.pp206-214 fatcat:7s7c3wub3zayvl42h2zxlmhxda