Implementing Binary Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Implementing Binary Neural Networks write by Joshua Wolff Fromm. This book was released on 2020. Implementing Binary Neural Networks available in PDF, EPUB and Kindle. The recent renaissance of deep neural networks has lead to impressive advancements in many domains of machine learning. However, the computational cost of these neural models in- creases in line with their performance, with many state-of-the-art models only being able to run on expensive high-end hardware. The need to efficiently deploy neural networks to commodity platforms has made network optimization a popular field of research. One particularly promising technique is network binarization, which quantizes the weights and activations of a model to only one or two bits. Although binarization offers theoretical oper- ation count reductions of up to 32X, no actual measurements have been reported. This is a symptom of the gap between theory and implementation of binary networks that exists to- day. In this work, we bridge the gap between abstract simulations and real usable high speed networks. To do so, we identify errors in the existing literature, develop novel algorithms, and introduce Riptide, an open source system that can train and deploy state-of-the-art binary neural networks to multiple hardware backends.
Neural Information Processing: Research and Development
Neural Information Processing: Research and Development - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Information Processing: Research and Development write by Jagath Chandana Rajapakse. This book was released on 2012-12-06. Neural Information Processing: Research and Development available in PDF, EPUB and Kindle. The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems. Even before the reincarnation of the field of artificial neural networks in mid nineteen eighties, researchers have attempted to explore the engineering of human brain function. After the reincarnation, we have seen an emergence of a large number of neural network models and their successful applications to solve real world problems. This volume presents a collection of recent research and developments in the field of neural information processing. The book is organized in three Parts, i.e., (1) architectures, (2) learning algorithms, and (3) applications. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights. The number of neurons and how they are connected to each other defines the architecture of a particular neural network. Part 1 of the book has nine chapters, demonstrating some of recent neural network architectures derived either to mimic aspects of human brain function or applied in some real world problems. Muresan provides a simple neural network model, based on spiking neurons that make use of shunting inhibition, which is capable of resisting small scale changes of stimulus. Hoshino and Zheng simulate a neural network of the auditory cortex to investigate neural basis for encoding and perception of vowel sounds.
Binary Neural Networks
Binary Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Binary Neural Networks write by Baochang Zhang. This book was released on 2023-12-13. Binary Neural Networks available in PDF, EPUB and Kindle. Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification, speech recognition, object detection, and more
Design of Asynchronous Binary Neural Networks Using Linear Programming
Design of Asynchronous Binary Neural Networks Using Linear Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Design of Asynchronous Binary Neural Networks Using Linear Programming write by Kerem Irten. This book was released on 1992. Design of Asynchronous Binary Neural Networks Using Linear Programming available in PDF, EPUB and Kindle.
Improved Grover's Implementation of Quantum Binary Neural Networks
Improved Grover's Implementation of Quantum Binary Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improved Grover's Implementation of Quantum Binary Neural Networks write by Brody A Wrighter. This book was released on 2023. Improved Grover's Implementation of Quantum Binary Neural Networks available in PDF, EPUB and Kindle. "Binary Neural Networks (BNNs) are the result of a simplification of network parameters in Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases significantly as the size of the network increases. This complexity can be greatly reduced if the parameters of the network are binarized. Binarization, which is a one bit quantization, can also come with complications including quantization error and information loss. The implementation of BNNs on quantum hardware could potentially provide a computational advantage over its classical counterpart. This is due to the fact that binarized parameters fit nicely to the nature of quantum hardware. Quantum superposition allows the network to be trained more efficiently, without using back propagation techniques, with the application of Grover’s Algorithm for the training process. This thesis presents two BNN designs that utilize only quantum hardware, and provides practical implementations for both of them. Looking into their scalability, improvements on the design are proposed to reduce complexity even further."--Abstract.
