Titlebook: Efficient Processing of Deep Neural Networks; Vivienne Sze,Yu-Hsin Chen,Joel S. Emer Book 2020 Springer Nature Switzerland AG 2020

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Hartwig Steffenhagen,Michael Stillere notion of the . of the computation for a particular workload layer shape onto a specific DNN accelerator design, and the fact that the compiler-like process of picking the right mapping is important to optimize behavior with respect to energy efficiency and/or performance.

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D. Bach,R. Hartung,W. Vahlensiecks well as the transfer of the data. The associated physical factors also limit the bandwidth available to deliver data between memory and compute, and thus limits the throughput of the overall system. This is commonly referred to by computer architects as the “memory wall.”

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1935-3235 lying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary wor978-3-031-00638-8978-3-031-01766-7Series ISSN 1935-3235 Series E-ISSN 1935-3243

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Overview of Deep Neural Networksapidly to improve accuracy and efficiency. In all cases, the input to a DNN is a set of values representing the information to be analyzed by the network. For instance, these values can be pixels of an image, sampled amplitudes of an audio wave, or the numerical representation of the state of some s

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Key Metrics and Design Objectiveskey metrics that one should consider when comparing and evaluating the strengths and weaknesses of different designs and proposed techniques and that should be incorporated into design considerations. While efficiency is often only associated with the number of operations per second per Watt (e.g.,

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