Chenxu Wang, Yunjie Deng, Zhenyu Ning, Kevin Leach, Jin Li, Shoumeng Yan, Zheng-hao He, Jiannong Cao, Fengwei Zhang
{"title":"Building a Lightweight Trusted Execution Environment for Arm GPUs","authors":"Chenxu Wang, Yunjie Deng, Zhenyu Ning, Kevin Leach, Jin Li, Shoumeng Yan, Zheng-hao He, Jiannong Cao, Fengwei Zhang","doi":"10.1109/TDSC.2023.3334277","DOIUrl":null,"url":null,"abstract":"A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation. However, Arm GPU security has not been explored by the community. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, but there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs. There is a need for generalizable and efficient Arm-based GPU security mechanisms. To address these problems, we present StrongBox, the first GPU TEE for secured general computation on Arm endpoints. StrongBox provides an isolated execution environment by ensuring exclusive access to GPU. Our approach is based in part on a dynamic, fine-grained memory protection policy as Arm-based GPUs typically share a unified memory with the CPU. Furthermore, StrongBox reduces runtime overhead from the redundant security introspection operations. We also design an effective defense mechanism within secure world to protect the confidential GPU computation. Our design leverages the widely-deployed Arm TrustZone and generic Arm features, without hardware modification or architectural changes. We prototype StrongBox using an off-the-shelf Arm Mali GPU and perform an extensive evaluation. Results show that StrongBox successfully ensures GPU computation security with a low (4.70%–15.26%) overhead.","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"8 5","pages":"3801-3816"},"PeriodicalIF":4.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Polymer Materials","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/TDSC.2023.3334277","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation. However, Arm GPU security has not been explored by the community. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, but there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs. There is a need for generalizable and efficient Arm-based GPU security mechanisms. To address these problems, we present StrongBox, the first GPU TEE for secured general computation on Arm endpoints. StrongBox provides an isolated execution environment by ensuring exclusive access to GPU. Our approach is based in part on a dynamic, fine-grained memory protection policy as Arm-based GPUs typically share a unified memory with the CPU. Furthermore, StrongBox reduces runtime overhead from the redundant security introspection operations. We also design an effective defense mechanism within secure world to protect the confidential GPU computation. Our design leverages the widely-deployed Arm TrustZone and generic Arm features, without hardware modification or architectural changes. We prototype StrongBox using an off-the-shelf Arm Mali GPU and perform an extensive evaluation. Results show that StrongBox successfully ensures GPU computation security with a low (4.70%–15.26%) overhead.
各种 Arm 终端利用集成和离散 GPU 加速计算。然而,业界尚未对 Arm GPU 的安全性进行探讨。现有工作使用可信执行环境(TEE)来解决基于英特尔平台的 GPU 安全问题,但由于存在大量架构差异,为 Arm GPU 部署 TEE 会面临新的技术挑战。我们需要可通用且高效的基于 Arm 的 GPU 安全机制。为了解决这些问题,我们推出了 StrongBox,它是首个在 Arm 端点上进行安全通用计算的 GPU TEE。StrongBox 通过确保对 GPU 的独占访问,提供了一个隔离的执行环境。我们的方法部分基于动态、细粒度的内存保护策略,因为基于 Arm 的 GPU 通常与 CPU 共享统一的内存。此外,StrongBox 还减少了冗余安全反省操作带来的运行时开销。我们还在安全世界中设计了一种有效的防御机制,以保护机密的 GPU 计算。我们的设计利用了广泛部署的 Arm TrustZone 和通用 Arm 功能,无需修改硬件或架构。我们使用现成的 Arm Mali GPU 制作了 StrongBox 原型,并进行了广泛的评估。结果表明,StrongBox 以较低的开销(4.70%-15.26%)成功确保了 GPU 计算的安全性。
期刊介绍:
ACS Applied Polymer Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics, and biology relevant to applications of polymers.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates fundamental knowledge in the areas of materials, engineering, physics, bioscience, polymer science and chemistry into important polymer applications. The journal is specifically interested in work that addresses relationships among structure, processing, morphology, chemistry, properties, and function as well as work that provide insights into mechanisms critical to the performance of the polymer for applications.