Binary Neural Networks

Deep learning has achieved impressive results in image classification, computer vision, and nat-

ural language processing. To achieve better performance, deeper and wider networks have been

designed, which increase the demand for computational resources. The number of floating-point

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 commu-

nity. We will describe numerous methods, including parameter quantization, network pruning,

low-rank decomposition, and knowledge distillation. More recently, to reduce (from binary to

low-bit) 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 per-

formance in various applications, such as image classification and object detection. We also

describe extensive applications of compressed deep models on image classification, speech rec-

ognition, 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

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Reviews recent advances in CNN compression and acceleration

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Elaborates recent advances on binary neural network (BNN) technologies

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Introduces applications of BNN in image classification, speech recognition, object detec-

tion, and more