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Deep convolutional neural networks (CNN) have shown great accuracy on object recognition and classification tasks. Deep CNNs are computation intensive algorithms, hence many customized RRAM crossbar-based accelerators are proposed to meet the computing demands in deep CNNs, but the area costs and the power consumption are still great challenges for RRAM crossbar-based accelerators. In this work, we...
Deep convolutional neural networks (DCNNs) have been successfully used in many computer vision tasks. Previous works on DCNN acceleration usually use a fixed computation pattern for diverse DCNN models, leading to imbalance between power efficiency and performance. We solve this problem by designing a DCNN acceleration architecture called deep neural architecture (DNA), with reconfigurable computation...
The coarse-grained reconfigurable architecture (C-GRA) is a promising platform that provides both high performance and high power-efficiency. Dataflow graph (DFG) mapping is critical to tap the potentials of CGRAs. Inspired from the great progress made in tree search game using deep neural network, we proposed a frame work for learning convolutional neural network for mapping DFGs onto spatial programmable...
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