arXiv AI August 25, 2026 By Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky, Sadat Shahriyar, Souvik Kundu, Zhishan Guo, Abdullah Al Arafat, Adnan Siraj Rakin

TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge

Read the original on arXiv AI →

TEE-X is a TEE‑aware acceleration framework designed to run large vision models, such as Vision Transformers, entirely within Trusted Execution Environments. It introduces a sensitivity‑aware modularization technique and vectorization to overcome memory constraints and latency challenges on edge devices. The framework is validated on OP‑TEE for Arm TrustZone and optimized for the NVIDIA Jetson AGX Xavier, achieving GPU‑level inference latency with minimal accuracy‑latency trade‑offs.

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By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak

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