Compares domain-aware, case-based, and feature attribution explainability methods for gate-level hardware Trojan detection on the Trust-Hub benchmark dataset.
In: 2024 IEEE 3rd International Conference on Com- puting and Machine Intelligence (ICMI), pp
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TwinLiteNet+ is a hybrid-encoder multi-task segmentation model with new UCB, USB, and PCAA modules that reports 92.9% mIoU on drivable area and 34.2% IoU on lane segmentation on BDD100K while using 11x fewer FLOPs than prior models.
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Explainability Methods for Hardware Trojan Detection: A Systematic Comparison
Compares domain-aware, case-based, and feature attribution explainability methods for gate-level hardware Trojan detection on the Trust-Hub benchmark dataset.
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TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving
TwinLiteNet+ is a hybrid-encoder multi-task segmentation model with new UCB, USB, and PCAA modules that reports 92.9% mIoU on drivable area and 34.2% IoU on lane segmentation on BDD100K while using 11x fewer FLOPs than prior models.