A Comprehensive Survey of Hardware Security in AI Accelerators: A Lifecycle-Aligned Taxonomy of Threats, Defenses, and Emerging Paradigms
The growth of machine learning (ML) and deep learning (DL) has resulted in the rapid development and implementation of dedicated hardware accelerators, including GPUs, TPUs, FPGAs, and custom ASICs. These accelerators maximize performance and efficiency yet present novel hardware level security vuln...
Furkejuvvon:
| Váldodahkkit: | , |
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| Materiálatiipa: | Artigo |
| Giella: | Inglês |
| Almmustuhtton: |
IEEE
2026-01-01
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| Ráidu: | IEEE Access |
| Fáttát: | |
| Liŋkkat: | https://ieeexplore.ieee.org/document/11556079/ |
| Fáddágilkorat: |
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