From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks - Couverture souple

Juracy, Leonardo Rezende; Garibotti, Rafael; Moraes, Fernando Gehm

Couverture souple

ISBN 10 : 1638281629 ISBN 13 : 9781638281627

Editeur : now publishers Inc, 2023

Synopsis

Over the past decade, a massive proliferation of machine learning algorithms has emerged, from applications for surveillance to self-driving cars. The turning point occurred with the arrival of Convolutional Neural Network (CNN) models and the incredible accuracy brought by Deep Neural Networks (DNNs) at the cost of high computational complexity. In this growing environment, graphic processing units (GPUs) have become the de facto reference platform for the training and inference phases of CNNs and DNNs due to their high processing parallelism and memory bandwidth. However, GPUs are power-hungry architectures. To enable the deployment of CNN and DNN applications on energy-constrained devices (e.g., IoT devices), industry and academic research have moved towards hardware accelerators. Following the evolution of neural networks (from CNNs to DNNs), this survey sheds light on the impact of this architectural shift and discusses hardware accelerator trends in terms of design, exploration, simulation, and frameworks developed in both academia and industry.

Détails de l'édition

• Éditeur: now publishers Inc
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 1638281629
• ISBN 13: 9781638281627
• Reliure: Broché
• Nombre de pages: 88
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible