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

HPA533

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Introductionstical learning on a large amount of data to obtain an effective representation of an input space. This is different from earlier approaches that use hand-crafted features or rules designed by experts.

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Key Metrics and Design Objectivescs including accuracy, throughput, latency, energy consumption, power consumption, cost, flexibility, and scalability. Reporting a comprehensive set of these metrics is important in order to provide a complete picture of the trade-offs made by a proposed design or technique.

Limited

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Designing Efficient DNN Models other co-design approaches, the main challenge is to improve the efficiency of the network architecture as evaluated by the metrics described in Chapter 3, such as energy consumption and latency, without sacrificing the accuracy.

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