Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlighted

翻译：深度神经网络已成为众多人工智能应用的关键支撑技术。在自动驾驶、医疗健康等领域，深度神经网络有时甚至展现出超越人类的表现。由于其计算复杂性，在资源受限设备上部署深度神经网络仍面临计算复杂度、能效、延迟和成本等多重挑战。为此，学术界和工业界正从多个研究方向探索加速和高效实现深度神经网络的方法。其中重要方向之一是为深度神经网络处理中海量数据确定合适的数据表示方式。传统数制已被证明对深度神经网络并非最优选择。另一方面，大量研究聚焦于探索合适的数制。本文旨在全面综述和讨论用于更高效表示深度神经网络数据的替代数制。文中探讨了深度神经网络中采用的各种数制（传统/非传统），并分析了这些数制对深度神经网络性能与硬件设计的影响。此外，本文还指出了每种数制面临的挑战以及针对这些挑战提出的解决方案。读者将能理解高效数制对深度神经网络的重要性，了解深度神经网络中广泛使用的数制，掌握不同数制间的权衡关系，并思考影响数制对深度神经网络性能作用的各种设计因素。最后，本文还将展望近期趋势及相关研究机遇。