As “deep neural networks” (DNNs) achieve increasing accuracy, they are getting employed in increasingly diverse applications, including security-critical applications such as medical and defense. This immense use of DNNs has motivated the researchers to scrutinizingly study their security vulnerability and propose countermeasures, especially in the context of hardware security. In this paper, we present a survey of techniques for the hardware security of DNNs. For the research works, we highlight the threat-model, key idea for launching attack and defense strategies. We organize the works on salient categories to highlight their strengths and limitations. This paper aims to equip researchers with the knowledge of recent advances in DNN security and motivate them to think of security as the first principle.

随着“深度神经网络”（DNN）的准确性不断提高，它们已被越来越多的应用所采用，包括医疗和国防等对安全至关重要的应用。大量使用DNN促使研究人员仔细研究其安全漏洞并提出对策，尤其是在硬件安全的情况下。在本文中，我们对DNN的硬件安全性技术进行了概述。对于研究工作，我们重点介绍了威胁模型，启动攻击和防御策略的关键思想。我们组织针对突出类别的作品，以突出其优势和局限性。本文旨在为研究人员提供有关DNN安全性最新进展的知识，并激发他们将安全性视为首要原则。