Data science and social research : epistemology, methods, technology and applications / N.Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino

• Record Nr.: UNINA9910355960303321
• Autore: Lauro, Natale Carlo
• Pubbl/distr/stampa: Cham, : Springer, 2017
• ISBN: 9783319554761
• Descrizione fisica: 300 p. ; 23 cm
• Altri autori (Persone): Amaturo, Enrica
• Disciplina: 300.72
• Locazione: BFS
• Collocazione: 300.72 LAU 1
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Accelerators for Convolutional Neural Networks

• Record Nr.: UNINA9911019510703321
• Autore: Munir Arslan
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2023; ©2024
• ISBN: 9781394171897; 9781394171910
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (307 pages)
• Altri autori (Persone): KongJoonho; QureshiMahmood Azhar
• Disciplina: 006.32
• Soggetti: Neural networks (Computer science); Computer architecture
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Part I Overview -- Chapter 1 Introduction -- 1.1 History and Applications -- 1.2 Pitfalls of High‐Accuracy DNNs/CNNs -- 1.2.1 Compute and Energy Bottleneck -- 1.2.2 Sparsity Considerations -- 1.3 Chapter Summary -- Chapter 2 Overview of Convolutional Neural Networks -- 2.1 Deep Neural Network Architecture -- 2.2 Convolutional Neural Network Architecture -- 2.2.1 Data Preparation -- 2.2.2 Building Blocks of CNNs -- 2.2.2.1 Convolutional Layers -- 2.2.2.2 Pooling Layers -- 2.2.2.3 Fully Connected Layers -- 2.2.3 Parameters of CNNs -- 2.2.4 Hyperparameters of CNNs -- 2.2.4.1 Hyperparameters Related to Network Structure -- 2.2.4.2 Hyperparameters Related to Training -- 2.2.4.3 Hyperparameter Tuning -- 2.3 Popular CNN Models -- 2.3.1 AlexNet -- 2.3.2 VGGNet -- 2.3.3 GoogleNet
• Sommario/riassunto: This book provides an in-depth exploration of accelerators for convolutional neural networks (CNNs), a pivotal component in the field of artificial intelligence and computer vision. It covers the architecture of CNNs, compressive coding techniques, and the design of both dense and sparse CNN accelerators. The text discusses hardware and software ; co-design and scheduling strategies to optimize CNN performance. Aimed at students, researchers, and professionals in computer architecture and hardware design, the book serves as a comprehensive reference on the development and implementation of CNN accelerators.