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Hardware Architectures for Deep Learning

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Editors: Masoud Daneshtalab 1 ; Mehdi Modarressi 2
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Publication Year: 2020

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.

Inspec keywords: recurrent neural nets; feedforward neural nets; learning (artificial intelligence); neuromorphic engineering; neural net architecture; neural chips

Other keywords: hardware architectures; embedded systems; model sparsity; deep learning hardware; error-tolerance; recurrent neural network; RNN; analog accelerators; feedforward models; low-precision data representation; hardware accelerators; convolutional neural networks; ultra-low-power IoT smart applications; inverter-based memristive neuromorphic circuit; stochastic data representations; binary data representations

Subjects: Neural nets (circuit implementations); Knowledge engineering techniques; Neural computing techniques; Parallel architecture; General and management topics; General electrical engineering topics; Neural net devices

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