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This book provides an overview of the basic concepts and recent advances in some of the key deep learning hardware research areas. Part I Deep learning and neural networks: concepts and models Part II Deep learning and approximate data representation Part III Deep learning and model sparsity Part IV Convolutional neural networks for embedded systems PartV Deep learning on analog accelerators
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