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.

翻译：深度学习（DL）近年来显著推动了智能系统的发展，并被广泛应用于众多实际场景。尽管其具有诸多优势与潜力，但在计算能力和能耗受限的设备上实施深度学习处理的需求依然极高。探索二元神经网络（BNN）这类颠覆性技术以增强深度学习能力自然而然地成为研究重点。近年来，BNN取得了显著进展，因其可在资源极其受限的设备上实现与部署，大幅降低存储需求、计算成本与能耗。然而，几乎所有BNN方法均需在额外存储、计算开销与性能提升之间进行权衡。本文对BNN领域的最新进展进行全面综述，与以往混合讨论低位宽工作的综述不同，本综述仅聚焦于1位激活值与权重的1位卷积网络。本文系统梳理了BNN的发展脉络——从早期前身到最新算法与技术，提出了完整的设计流程，并讨论了各模块的变体。在此过程中，本文深入探讨了BNN的：（a）目标：早期成功与挑战；（b）优化：精选包含核心优化技术的代表性工作；（c）部署：用于BNN建模与开发的开源框架；（d）终端：面向BNN的高效计算架构与设备；（e）应用：BNN的多样化应用场景。此外，本文各章节均讨论了潜在研究方向与未来研究机遇。