Hardware Architectures for Deep Learning

AvMasoud, Daneshtalab,Masoud Daneshtalab

Inbunden, Engelska, 2020

Del i serien Materials, Circuits and Devices

1 671 kr

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Beskrivning

This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency.Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.

Produktinformation

Utgivningsdatum:2020-04-24
Mått:156 x 234 x 20 mm
Vikt:680 g
Format:Inbunden
Språk:Engelska
Serie:Materials, Circuits and Devices
Antal sidor:328
Förlag:Institution of Engineering and Technology
ISBN:9781785617683

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

Masoud Daneshtalab is a tenured associate professor at Mälardalen University (MDH) in Sweden, an adjunct professor at Tallinn University of Technology (TalTech) in Estonia, and sits on the board of directors of Euromicro. His research interests include interconnection networks, brain-like computing, and deep learning architectures. He has published over 300-refereed papers.Mehdi Modarressi is an assistant professor at the Department of Electrical and Computer Engineering, University of Tehran, Iran. He is the founder and director of the Parallel and Network-based Processing research laboratory at the University of Tehran, where he leads several industrial and research projects on deep learning-based embedded system design and implementation.

Innehållsförteckning

Part I: Deep learning and neural networks: concepts and modelsChapter 1: An introduction to artificial neural networksChapter 2: Hardware acceleration for recurrent neural networksChapter 3: Feedforward neural networks on massively parallel architectures Part II: Deep learning and approximate data representationChapter 4: Stochastic-binary convolutional neural networks with deterministic bit-streamsChapter 5: Binary neural networks Part III: Deep learning and model sparsityChapter 6: Hardware and software techniques for sparse deep neural networksChapter 7: Computation reuse-aware accelerator for neural networks Part IV: Convolutional neural networks for embedded systemsChapter 8: CNN agnostic accelerator design for low latency inference on FPGAsChapter 9: Iterative convolutional neural network (ICNN): an iterative CNN solution for low power and real-time systems Part V: Deep learning on analog acceleratorsChapter 10: Mixed-signal neuromorphic platform design for streaming biomedical signal processingChapter 11: Inverter-based memristive neuromorphic circuit for ultra-low-power IoT smart applications