New framework optimizes hardware for adversarial robustness at lower cost

A new framework has been developed to help optimize the selection of hardware and hyper-parameters for adversarial robust machine learning systems. This framework uses accelerated failure time (AFT) models to quantify the impact of factors like hardware choice, batch size, and epochs on model survival time. Experiments indicate that the Nvidia L4 GPU offers a significant increase in adversarial survival time at a much lower cost compared to the V100, suggesting that expensive hardware is not always necessary for improved robustness. The research also found that inference latency is a more critical predictor of adversarial robustness than training time or hardware configuration. AI

IMPACT Provides a cost-effective method for selecting hardware and tuning models to improve adversarial robustness, potentially lowering deployment costs for AI systems.

RANK_REASON Academic paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Charles Meyers
• graphics processing unit
• Nvidia L4

• paper
• infra

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