SecureLoop: Design Space Exploration of Secure DNN Accelerators
Name
3613424.3614273.pdf
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1.33 MB
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Adobe PDF
Checksum (MD5)
806b7810efb990b9a5c1461a6d20768d
Author(s)
Lee, Kyungmi
Yan, Mengjia
Emer, Joel
Chandrakasan, Anantha P
Date Issued
October 28, 2023
Publisher
ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture
Citation
Lee, Kyungmi, Yan, Mengjia, Emer, Joel and Chandrakasan, Anantha. 2023. "SecureLoop: Design Space Exploration of Secure DNN Accelerators."
Version
Final published version
Abstract
Deep neural networks (DNNs) are gaining popularity in a wide range of domains, ranging from speech and video recognition to healthcare. With this increased adoption comes the pressing need for securing DNN execution environments on CPUs, GPUs, and ASICs. While there are active research efforts in supporting a trusted execution environment (TEE) on CPUs, the exploration in supporting TEEs on accelerators is limited, with only a few solutions available [18, 19, 27]. A key limitation along this line of work is that these secure DNN accelerators narrowly consider a few specific architectures. The design choices and the associated cost for securing these architectures do not transfer to other diverse architectures.
This paper strives to address this limitation by developing a design space exploration tool for supporting TEEs on diverse DNN accelerators. We target secure DNN accelerators equipped with cryptographic engines where the cryptographic operations are closely coupled with the data movement in the accelerators. These operations significantly complicate the scheduling for DNN accelerators, as the scheduling needs to account for the extra on-chip computation and off-chip memory accesses introduced by these cryptographic operations, and even needs to account for potential interactions across DNN layers.
We tackle these challenges in our tool, called SecureLoop, by introducing a scheduling search engine with the following attributes: 1) considers the cryptographic overhead associated with every off-chip data access, 2) uses an efficient modular arithmetic technique to compute the optimal authentication block assignment for each individual layer, and 3) uses a simulated annealing algorithm to perform cross-layer optimizations. Compared to the conventional schedulers, our tool finds the schedule for secure DNN designs with up to 33.2% speedup and 50.2% improvement of energy-delay-product.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3613424.3614273
