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Communication Dans Un Congrès Année : 2024

Reliability and Security of AI Hardware

Résumé

In recent years, Artificial Intelligence (AI) systems have achieved revolutionary capabilities, providing intelligent solutions that surpass human skills in many cases. However, such capabilities come with power-hungry computation workloads. Therefore, the implementation of hardware acceleration becomes as fundamental as the software design to improve energy efficiency, silicon area, and latency of AI systems. Thus, innovative hardware platforms, architectures, and compiler-level approaches have been used to accelerate AI workloads. Crucially, innovative AI acceleration platforms are being adopted in application domains for which dependability must be paramount, such as autonomous driving, healthcare, banking, space exploration, and industry 4.0. Unfortunately, the complexity of both AI software and hardware makes the dependability evaluation and improvement extremely challenging. Studies have been conducted on both the security and reliability of AI systems, such as vulnerability assessments and countermeasures to random faults and analysis for side-channel attacks. This paper describes and discusses various reliability and security threats in AI systems, and presents representative case studies along with corresponding efficient countermeasures.
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Dates et versions

hal-04577494 , version 1 (16-05-2024)

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  • HAL Id : hal-04577494 , version 1

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Dennis Gnad, Jonas Krautter, Angeliki Kritikakou, Vincent Meyers, Paolo Rech, et al.. Reliability and Security of AI Hardware. ETS 2024 - 29th IEEE European Test Symposium, May 2024, The Hague, Netherlands. pp.1-10. ⟨hal-04577494⟩
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