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Convolutional neural network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many computer vision algorithms. But the computation complexity of CNN is much higher than traditional algorithms. With the help of GPU acceleration, CNN-based applications are widely deployed in servers. However, for embedded platforms, CNN-based solutions are still...
Deep learning, and especially Convolutional Neural Network (CNN, is among the most powerful and widely used techniques in computer vision. Applications range from image classification to object detection, segmentation, Optical Character Recognition (OCR), etc. At the same time, CNNs are both computationally intensive and memory intensive, making them difficult to be deployed on low power lightweight...
As different tiers are stacked together in 3-D integrated circuits, the power/ground (P/G) network simulation becomes more challenging than that of 2-D cases. In this brief, we propose a hierarchical simulation method suitable for 3-D P/G network (HS3-DPG), which takes advantage of the inherent hierarchical structure of 3-D P/G network. The port equivalent model (PEM) is introduced to mask the details...
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