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Artificial Intelligence has witnessed tremendous growth with the advent of deep neural networks (DNNs) and Machine Learning. The algorithmic superiority of DNNs comes at extremely high computation and memory costs that pose both challenges and opportunities to the hardware platforms. GPUs (graphics processing units), NPUs (neural processing units), and specialized Hardware accelerators are the state-of-the-art in training and inference the DNNs. This edited book consisting of 11 chapters contributed by researchers from academia and industries from different countries explores research in Hardware Accelerator Systems to improve processing efficiency and performance of GPU, NPU, approximate computing, inference accelerators, in-memory computing, machine intelligence, and quantum computing. | 
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发表于 2022-11-20 00:28:51
