深度学习处理器正在崛起成为深度学习的重要处理平台，然而目前其设计尚缺乏对可靠性的保障，这也限制了深度学习的更广泛应用。随着制造工艺的微型化，硬件执行时发生错误的概率呈指数级增长，这成为了深度学习处理器可靠性的提升所面临的重要挑战。降低硬件执行错误的发生率，并实现错误的检测与恢复，是保障深度学习处理器可靠性的关键。本课题拟从分析和优化两个方面对深度学习处理器可靠性进行研究，兼顾性能及功耗，达到以低代价的方式提高深度学习处理器可靠性的研究目标。课题主要内容包括：建立深度学习处理器体系结构可靠性量化模型并分析可靠性特征；分析深度学习应用算法并提取其可靠性需求；研究低开销的硬件保护方法以提高深度学习处理器可靠性；基于高能效的错误检测及错误恢复设计优化深度学习处理器的可靠性。这一研究有助于提高深度学习处理器在自动驾驶、手术机器人等新兴领域的应用前景。

Recently, deep learning processors emerge as major computing platforms for deep learning applications. However, inadequate reliability features of current deep learning processors prevent their prevalence in self-driving cars, surgery robots, and so on. Unfortunately, as integrated circuit manufacturing process scales down, hardware error rate increases exponentially, which becomes a great challenge for reliable deep learning processing. Besides reducing error rate, error detection and recovery are also the key to improve the reliability of deep learning processors. In this proposal, we focus on the analysis and optimization regarding to the reliability of deep learning processors. The goal is to improve the reliability of deep learning processors without significant performance and energy loss. We model and analyze the reliability features of deep learning processor architectures in a quantitative approach, characterize the reliability requirements of deep learning algorithms, improve the reliability of deep learning processors via hardware protections, and optimize the reliability of deep learning processors through energy-efficient error detection and recovery mechanisms. This work will benefit the application of deep learning processors in emerging fields such as self-driving cars and surgery robots.