Neural Network based VLSI architecture for HEVC Motion Estimation

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This research focuses on using newest deep learning algorithms and its implementation to improve motion estimation in video coding as it tightly affects the video coding quality and the energy consumption.The first year focuses on the hardware design part, which is to design an energy-efficient hardware architecture of deep learning algorithms. Specifically, the deep convolution neural networks are chosen for their superior performance on many image/video related tasks.I put efforts on two things, the scheduling algorithm for computing order and new hardware architectures. Both are regarded as the important issues in this fields as they affects on energy costs, which is critical for mobile devices. Both works are accepted in international conferences.