Collaborative Research: SHF: Medium: Approximate Computing for Machine Learning Security: Foundations and Accelerator Design

深度神经网络(DNN)在大量和不断扩展的应用领域取得了最先进的性能。然而，其大规模部署面临的威胁之一是易受对抗性机器学习攻击，即对手向输入数据注入微小扰动，导致DNN错误分类，从而产生潜在危险的结果(例如，将停车标志误认为限速标志)。在这个项目中，研究人员将探索如何构建具有近似计算元素的DNN，以提高它们对这些对手攻击的健壮性。近似计算是一种技术，用于构建更简单(因此更高性能和更可持续)的计算元素，但不计算操作的确切结果。研究人员将探索如何选择近似计算元素并将其用于构建可持续的DNN加速器，以平衡性能、精度和安全性。该提案的预期贡献包括对DNN的近似和健壮性之间的关系提出新的见解。该项目将探索哪些类型的近似技术可以产生有效的DNN，以平衡准确性、性能、可持续性和对对手攻击的保护，并开发能够沿这些维度找到最佳运行点的优化框架。它还将探索如何构建专门针对此应用程序的新近似计算元素。该项目将利用这些发现来建造可持续、高性能和准确的DNN加速器。该项目还将探索其他基于近似计算的技术，以防止威胁DNN安全和隐私的其他类型的攻击，以及不同的深度神经网络学习结构。该项目预计将对机器学习模型的安全性、可持续性和准确性产生重大影响。研究团队将与研究团体分享研究的所有副产品。该项目将培养研究生和本科生。调查人员将开发新的教育材料，用于机器学习、计算机体系结构和计算机安全课程。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Deep Neural Networks (DNNs) are achieving state-of-the-art performance on a large and expanding number of application domains. However, one of the threats to their wide-scale deployment is vulnerability to adversarial machine learning attacks, where an adversary injects small perturbations to the input data that cause the DNN to misclassify, with potentially dangerous outcomes (for example, mistaking a stop sign for a speed limit sign). In this project, the researchers will explore how building DNNs with approximate computing elements improves their robustness to these adversarial attacks. Approximate computing is a technique to build computing elements that are simpler (and therefore higher performing and more sustainable) but do not compute the exact result of an operation. The investigators will explore how to select approximate computing elements and use them in building sustainable DNN accelerators that balance performance, accuracy, and security.The proposal's expected contributions include developing new insights into the relationship between approximation and robustness of DNNs. The project will explore what types of approximation techniques result in effective DNNs that balance accuracy, performance, sustainability, and protection against adversarial attacks and develop optimization frameworks that can find optimal operating points along these dimensions. It will also explore how to build new approximate computing elements specifically targeted toward this application. The project will use these findings to build sustainable, performant, and accurate DNN accelerators. The project will also explore other approximate computing-based techniques to protect against other types of attacks threatening the security and privacy of DNNs, as well as for different deep neural network learning structures. The project is expected to have significant impacts on security, sustainability, and accuracy of machine learning models. The research team will share all of the byproducts of the research with the research community. The project will train graduate and undergraduate students. The investigators will develop new educational material for use in machine learning, computer architecture, and computer security classes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.