Towards Secure Machine Learning Acceleration: Threats and Defenses Across Algorithms, Architecture, and Circuits
As deep neural networks (DNNs) are widely adopted for high-stakes applications that process sensitive private data and make critical decisions, security concerns about user data and DNN models are growing. In particular, hardware-level vulnerabilities can be exploited to undermine the confidentialit...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156346 |