@inproceedings{119c39486a6f420fa6de91cb522b4dff,
title = "AccShield: A New Trusted Execution Environment with Machine-Learning Accelerators",
abstract = "Machine learning accelerators such as the Tensor Processing Unit (TPU) are already being deployed in the hybrid cloud, and we foresee such accelerators proliferating in the future. In such scenarios, secure access to the acceleration service and trustworthiness of the underlying accelerators become a concern. In this work, we present AccShield, a new method to extend trusted execution environments (TEEs) to cloud accelerators which takes both isolation and multi-tenancy into security consideration. We demonstrate the feasibility of accelerator TEEs by a proof of concept on an FPGA board. Experiments with our prototype implementation also provide concrete results and insights for different design choices related to link encryption, isolation using partitioning and memory encryption.",
keywords = "Trusted execution environment (TEE), accelerator TEE, cloud computing, confidential computing",
author = "Wei Ren and William Kozlowski and Sandhya Koteshwara and Mengmei Ye and Hubertus Franke and Deming Chen",
note = "We would like to thank our colleagues at IBM and the University of Illinois Urbana-Champaign for their valuable insights and constructive comments. This work is supported by the IBM-Illinois Discovery Accelerator Institute (IIDAI).; 60th ACM/IEEE Design Automation Conference, DAC 2023 ; Conference date: 09-07-2023 Through 13-07-2023",
year = "2023",
doi = "10.1109/DAC56929.2023.10247768",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 60th ACM/IEEE Design Automation Conference, DAC 2023",
address = "United States",
}