Hardware Architectures for Deep Learning /

The book provides an overview of the basic concepts and recent advances in some of the key deep learning hardware research areas. The book is structured into five parts. Part I of the book contains three chapters to introduce the fundamental concepts of neural networks. This part first provides a ba...

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Bibliographic Details
Corporate Author:	Institution of Engineering and Technology
Other Authors:	Daneshtalab, Masoud (Editor), Modarressi, Mehdi (Editor)
Format:	eBook
Language:	English
Published:	Stevenage : IET, 2020.
Series:	Materials, Circuits & Devices.
Subjects:	Machine learning. Microprocessors. Neural computers. feedforward neural nets. learning (artificial intelligence) neural chips. neural net architecture. neuromorphic engineering. recurrent neural nets. hardware architectures ultra-low-power IoT smart applications inverter-based memristive neuromorphic circuit analog accelerators embedded systems convolutional neural networks model sparsity hardware accelerators error-tolerance binary data representations stochastic data representations low-precision data representation feedforward models RNN recurrent neural network deep learning hardware Electronic books.
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