Anmerkungen:
Includes bibliographical references and index (pages 297-306)
Beschreibung:
Intro -- Contents -- About the editors -- Preface -- Acknowledgments -- Part I. Deep learning and neural networks: concepts and models -- 1. An introduction to artificial neural networks / Ahmad Kalhor -- 1.1 Introduction -- 1.1.1 Natural NNs -- 1.1.2 Artificial neural networks -- 1.1.3 Preliminary concepts in ANNs -- 1.2 ANNs in classification and regression problems -- 1.2.1 ANNs in classification problems -- 1.2.2 ANNs in regression problems -- 1.2.3 Relation between classification and regression -- 1.3 Widely used NN models -- 1.3.1 Simple structure networks
1.3.2 Multilayer and deep NNs -- 1.4 Convolutional neural networks -- 1.4.1 Convolution layers -- 1.4.2 Pooling layers -- 1.4.3 Learning in CNNs -- 1.4.4 CNN examples -- 1.5 Conclusion -- References -- 2. Hardware acceleration for recurrent neural networks / Sima Sinaei and Masoud Daneshtalab -- 2.1 Recurrent neural networks -- 2.1.1 Long short-term memory -- 2.1.2 Gated recurrent units -- 2.2 Hardware acceleration for RNN inference -- 2.2.1 Software implementation -- 2.2.2 Hardware implementation -- 2.3 Hardware implementation of LSTMs -- 2.3.1 Model compression
2.3.2 Datatype and Quantization -- 2.3.3 Memory -- 2.4 Conclusion -- References -- 3. Feedforward neural networks on massively parallel architectures / Reza Hojabr, Ahmad Khonsari, Mehdi Modarressi, and Masoud Daneshtalab -- 3.1 Related work -- 3.2 Preliminaries -- 3.3 ClosNN: a customized Clos for neural network -- 3.4 Collective communications on ClosNN -- 3.5 ClosNN customization and area reduction -- 3.6 Folded ClosNN -- 3.7 Leaf switch optimization -- 3.8 Scaling to larger NoCs -- 3.9 Evaluation -- 3.9.1 Performance comparison under synthetic traffic
3.9.2 Performance evaluation under realistic workloads -- 3.9.3 Power comparison -- 3.9.4 Sensitivity to neural network size -- 3.10 Conclusion -- References -- Part II. Deep learning and approximate data representation -- 4. Stochastic-binary convolutional neural networks with deterministic bit-streams / M. Hassan Najafi, S. Rasoul Faraji, Bingzhe Li, David J. Lilja, and Kia Bazargan -- 4.1 Overview -- 4.2 Introduction -- 4.3 Background -- 4.3.1 Stochastic computing -- 4.3.2 Deterministic low-discrepancy bit-streams -- 4.3.3 Convolutional neural networks -- 4.4 Related work
Part III. Deep learning and model sparsity -- Part IV. Convolutional neural networks for embedded systems -- Part V. Deep learning on analog accelerators.
This book discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The book provides an overview of this emerging field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms