Confidential Execution of Deep Learning Inference at the Untrusted Edge with ARM TrustZone

Author:

Islam Md Shihabul1ORCID,Zamani Mahmoud1ORCID,Kim Chung Hwan1ORCID,Khan Latifur1ORCID,Hamlen Kevin W.1ORCID

Affiliation:

1. The University of Texas at Dallas, Richardson, TX, USA

Funder

ONR

DARPA

NSF

ARO

Publisher

ACM

Reference52 articles.

1. On the Performance of ARM TrustZone

2. Andrew Anderson , Aravind Vasudevan , Cormac Keane , and David Gregg . 2017. Low-memory Gemm-based Convolution Algorithms for Deep Neural Networks. arXiv Preprint 1709.03395 ( 2017 ). Andrew Anderson, Aravind Vasudevan, Cormac Keane, and David Gregg. 2017. Low-memory Gemm-based Convolution Algorithms for Deep Neural Networks. arXiv Preprint 1709.03395 (2017).

3. ARM. 2009. ARM Security Technology: Building a Secure System using TrustZone Technology. White paper PRD29-GENC-009492C. ARM. ARM. 2009. ARM Security Technology: Building a Secure System using TrustZone Technology. White paper PRD29-GENC-009492C. ARM.

4. Web Navigation Prediction Using Multiple Evidence Combination and Domain Knowledge

5. Keith Bonawitz , Hubert Eichner , Wolfgang Grieskamp , Dzmitry Huba , Alex Ingerman , Vladimir Ivanov , Chloé Kiddon , Jakub Konevc nỳ, Stefano Mazzocchi , Brendan McMahan , Timon Van Overveldt , David Petrou , Daniel Ramage , and Jason Roselander . 2019 . Towards Federated Learning at Scale: System Design . In Proc. Machine Learning and Systems nymMLSys. Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konevc nỳ, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019. Towards Federated Learning at Scale: System Design. In Proc. Machine Learning and Systems nymMLSys.

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1. Hardware Support for Trustworthy Machine Learning: A Survey;2024 25th International Symposium on Quality Electronic Design (ISQED);2024-04-03

2. TEEm: Supporting Large Memory for Trusted Applications in ARM TrustZone;IEEE Access;2024

3. Edge AI on Constrained IoT Devices: Quantization Strategies for Model Optimization;Lecture Notes in Networks and Systems;2024

4. Trusted Deep Neural Execution—A Survey;IEEE Access;2023

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