Deep learning (DL) has recently changed the development of intelligent
systems and is widely adopted in many real-life applications. Despite their
various benefits and potentials, there is a high demand for DL processing in
different computationally limited and energy-constrained devices. It is natural
to study game-changing technologies such as Binary Neural Networks (BNN) to
increase deep learning capabilities. Recently remarkable progress has been made
in BNN since they can be implemented and embedded on tiny restricted devices
and save a significant amount of storage, computation cost, and energy
consumption. However, nearly all BNN acts trade with extra memory, computation
cost, and higher performance. This article provides a complete overview of
recent developments in BNN. This article focuses exclusively on 1-bit
activations and weights 1-bit convolution networks, contrary to previous
surveys in which low-bit works are mixed in. It conducted a complete
investigation of BNN's development -from their predecessors to the latest BNN
algorithms/techniques, presenting a broad design pipeline and discussing each
module's variants. Along the way, it examines BNN (a) purpose: their early
successes and challenges; (b) BNN optimization: selected representative works
that contain essential optimization techniques; (c) deployment: open-source
frameworks for BNN modeling and development; (d) terminal: efficient computing
architectures and devices for BNN and (e) applications: diverse applications
with BNN. Moreover, this paper discusses potential directions and future
research opportunities in each section.

本文全面评述了二值神经网络（BNN）的发展历程，从祖先算法到最新算法 / 技术，提出了一种广泛的设计流程，并讨论了每个模块的变体。此外，还介绍了 BNN 的应用，以及其潜在的发展方向和未来研究机会。