Recorded CVPR’21 CV-AML Workshop Outstanding Paper Talk “Bit Error Robustness for Energy-Efficient DNN Accelerators”

In June this year, my work on bit error robustness of deep neural networks (DNNs) was recognized as outstanding paper at the CVPR’21 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (AML-CV). Thus, as part of the workshop, I prepared a 15 minute talk highlighting how robustness against bit errors in DNN weights can improve the energy-efficiency of DNN accelerators. In this article, I want to share the recording.

Paper on ArXivWorkshop Webpage


Due to German data protection laws, it is currently not possible to include the video directly, so please head over to YouTube using the link below!

Watch on YouTube
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