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From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks

Current price: $65.00
From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks
From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks

Barnes and Noble

From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks

Current price: $65.00
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The most popular ML models are brain-inspired models such as Neural Networks (NNs), including Convolutional Neural Networks (CNNs) and, more recently, Deep Neural Networks (DNNs). They are characterized by resembling the human brain, performing data processing by mimicking synapses using thousands of interconnected neurons in a network. In this growing environment, GPUs have become the de facto reference platform for the training and inference phases of CNNs and DNNs, due to their high processing parallelism and memory bandwidth. However, GPUs are power-hungry architectures. To enable the deployment of CNN and DNN applications on energy-constrained devices (e.g., IoT devices), industry and academic research have moved towards hardware accelerators. Following the evolution of neural networks from CNNs to DNNs, this monograph sheds light on the impact of this architectural shift and discusses hardware accelerator trends in terms of design, exploration, simulation, and frameworks developed in both academia and industry.

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Barnes & Noble does business -- big business -- by the book. As the #1 bookseller in the US, it operates about 720 Barnes & Noble superstores (selling books, music, movies, and gifts) throughout all 50 US states and Washington, DC. The stores are typically 10,000 to 60,000 sq. ft. and stock between 60,000 and 200,000 book titles. Many of its locations contain Starbucks cafes, as well as music departments that carry more than 30,000 titles.

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