Media type: E-Book Title: Efficient processing of deep neural networks Contributor: Sze, Vivienne [Author]; Cheng, Youxin [Author]; Yan, Tien-Ju [Author]; Emer, Joel S. [Author] Published: Cham: Springer, [2022] Published in: Synthesis lectures on computer architecture ; 50 Issue: Reprint of original edition Morgan & Claypool 2020 Extent: 1 Online-Ressource (333 Seiten) Language: English ISBN: 9783031017667 Keywords: Neural networks (Computer science) ; Electronic books Origination: Footnote: Description: Intro -- Preface -- Acknowledgments -- Understanding Deep Neural Networks -- Introduction -- Background on Deep Neural Networks -- Artificial Intelligence and Deep Neural Networks -- Neural Networks and Deep Neural Networks -- Training versus Inference -- Development History -- Applications of DNNs -- Embedded versus Cloud -- Overview of Deep Neural Networks -- Attributes of Connections Within a Layer -- Attributes of Connections Between Layers -- Popular Types of Layers in DNNs -- CONV Layer (Convolutional) -- FC Layer (Fully Connected) -- Nonlinearity -- Pooling and Unpooling -- Normalization -- Compound Layers -- Convolutional Neural Networks (CNNs) -- Popular CNN Models -- Other DNNs -- DNN Development Resources -- Frameworks -- Models -- Popular Datasets for Classification -- Datasets for Other Tasks -- Summary -- Design of Hardware for Processing DNNs -- Key Metrics and Design Objectives -- Accuracy -- Throughput and Latency -- Energy Efficiency and Power Consumption -- Hardware Cost -- Flexibility -- Scalability -- Interplay Between Different Metrics -- Kernel Computation -- Matrix Multiplication with Toeplitz -- Tiling for Optimizing Performance -- Computation Transform Optimizations -- Gauss' Complex Multiplication Transform -- Strassen's Matrix Multiplication Transform -- Winograd Transform -- Fast Fourier Transform -- Selecting a Transform -- Summary -- Designing DNN Accelerators -- Evaluation Metrics and Design Objectives -- Key Properties of DNN to Leverage -- DNN Hardware Design Considerations -- Architectural Techniques for Exploiting Data Reuse -- Temporal Reuse -- Spatial Reuse -- Techniques to Reduce Reuse Distance -- Dataflows and Loop Nests -- Dataflow Taxonomy -- Weight Stationary (WS) -- Output Stationary (OS) -- Input Stationary (IS) -- Row Stationary (RS) -- Other Dataflows -- Dataflows for Cross-Layer Processing.