AccShield: A New Trusted Execution Environment with Machine-Learning Accelerators

Wei Ren, William Kozlowski, Sandhya Koteshwara, Mengmei Ye, Hubertus Franke, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication2023 60th ACM/IEEE Design Automation Conference, DAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323481
StatePublished - 2023
Event60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States
Duration: Jul 9 2023Jul 13 2023

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Conference60th ACM/IEEE Design Automation Conference, DAC 2023
Country/TerritoryUnited States
CitySan Francisco


  • Trusted execution environment (TEE)
  • accelerator TEE
  • cloud computing
  • confidential computing

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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