From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks
Seiten
2023
now publishers Inc (Verlag)
978-1-63828-162-7 (ISBN)
now publishers Inc (Verlag)
978-1-63828-162-7 (ISBN)
Following the evolution of neural networks from CNNs to DNNs, this monograph 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.
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.
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.
1. Introduction
2. Basic Concepts
3. CNN and DNN Hardware Accelerators
4. Hardware Design Space Exploration Frameworks and Simulators
5. Conclusion
Acknowledgements
References
Erscheinungsdatum | 13.03.2023 |
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Reihe/Serie | Foundations and Trends® in Electronic Design Automation |
Verlagsort | Hanover |
Sprache | englisch |
Maße | 156 x 234 mm |
Gewicht | 137 g |
Themenwelt | Technik ► Elektrotechnik / Energietechnik |
ISBN-10 | 1-63828-162-9 / 1638281629 |
ISBN-13 | 978-1-63828-162-7 / 9781638281627 |
Zustand | Neuware |
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Buch | Hardcover (2023)
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49,99 €