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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Sze, Vivienne (VerfasserIn), Chen, Yu-Hsin (VerfasserIn), Yang, Tien-Ju (VerfasserIn), Emer, Joel S. (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	[San Rafael] Morgan & Claypool Publishers [2020]
Schriftenreihe:	Synthesis lectures on computer architecture #50
Schlagworte:	Neuronales Netz Lernendes System Maschinelles Lernen Deep learning Electronic books
Online-Zugang:	TUM01
Zusammenfassung:	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
Beschreibung:	Description based on publisher supplied metadata and other sources
Beschreibung:	1 Online-Ressource
ISBN:	9781681738321