MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators
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Abstract (arXiv)
This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators. MGX minimizes the performance overhead of off-chip memory encryption and integrity verification by exploiting the application-specific properties of the accelerator execution. In particular, accelerators tend to explicitly manage data movement between on-chip and off-chip memories. Therefore, the general memory access pattern of an accelerator can largely be determined for a given application. Exploiting these characteristics, MGX generates version numbers used in memory encryption and integrity verification using on-chip accelerator state rather than storing them in the off-chip memory; it also customizes the granularity of the memory protection to match the granularity used by the accelerator. To demonstrate the efficacy of MGX, we present an in-depth study of MGX for DNN and graph algorithms. Experimental results show that on average, MGX lowers the performance overhead of memory protection from 28% and 33% to 4% and 5% for DNN and graph processing accelerators in a wide range of benchmarks, respectively.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.
5 papers.
- 2023GPU-based Private Information Retrieval for On-Device Machine Learning Inferencecited by 5
- 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Acceleratorscited by 1
- 2024SoK: A Systems Perspective on Compound AI Threats and Countermeasurescited by 1
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computingcited by 0
- 2026Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Acceleratorscited by 0
- 2026Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators25 Apr 2026
- 2024SoK: A Systems Perspective on Compound AI Threats and Countermeasures20 Nov 2024
- 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators4 Sep 2024
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing12 Jul 2024
- 2023GPU-based Private Information Retrieval for On-Device Machine Learning Inference26 Jan 2023