IAM

TAG»DNN ACCELERATORS«

ARTICLE

Benchmarking Bit Errors in Quantized Neural Networks with PyTorch

Similar to my article series on adversarial robustness, I was planning to have a series on bit errors robustness accompanied by PyTorch code. Instead, due to time constraints, I decided to condense the information into a single article. The code for the originally planned six articles is available on GitHub.

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JUNE2023

PROJECT

OPEN SOURCE Bit Error Robustness in PyTorch Article Series I was planning to have an article series on bit error robustness in deep learning — similar to my article series on adversarial robustness — with accompanying PyTorch code. However, the recent progress in machine learning made me focus on other projects. Nevertheless, the articles should […]

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ARTICLE

PhD Thesis on Robustness and Uncertainty in Deep Learning

In March this year I finally submitted my PhD thesis and successfully defended in July. Now, more than 6 months later, my thesis is finally available in the university’s library. During my PhD, I worked on various topics surrounding robustness and uncertainty in deep learning, including adversarial robustness, robustness to bit errors, out-of-distribution detection and conformal prediction. In this article, I want to share my thesis and give an overview of its contents.

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NOVEMBER2022

PROJECT

PhD thesis on uncertainty estimation and (adversarial) robustness in deep learning.

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ARTICLE

PhD Defense Slides and Lessons Learned

In July this year I finally defended my PhD which mainly focused on (adversarial) robustness and uncertainty estimation in deep learning. In my case, the defense consisted of a (public) 30 minute talk about my work, followed by questions from the thesis committee and audience. In this article, I want to share the slides and some lessons learned in preparing for my defense.

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ARTICLE

Math Machine Learning Seminar of MPI MiS and UCLA Talk “Relating Adversarial Robustness and Weight Robustness Through Flatness”

In October, I had the pleasure to present my recent work on adversarial robustness and flat minima at the math machine learning seminar of MPI MiS and UCLA organized by Guido Montúfar. The talk covers several aspects of my PhD research on adversarial robustness and robustness in terms of the model weights. This article shares abstract and recording of the talk.

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JULY2021

PROJECT

Random and adversarial bit error robustness of DNNs for energy-efficient and secure DNN accelerators.

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ARTICLE

Qualcomm Innovation Fellowship Talk “Confidence-Calibrated Adversarial Training and Random Bit Error Training”

As part of the Qualcomm Innovation Fellowship 2019, I have a talk on the research produced throughout the academic year 2019/2020. This talk covers two exciting works on robustness: robustness against various types of adversarial examples using confidence-calibrated adversarial training (CCAT) and robustness against bit errors in the model’s quantized weights. The latter can be shown to be important to reduce the energy-consumption of accelerators for neural networks. In this article, I want to share the slides corresponding to the talk.

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ARTICLE

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.

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ARTICLE

ArXiv Pre-Print “Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators”

Deep neural network (DNN) accelerators are popular due to reduced cost and energy compared to GPUs. To further reduce energy consumption, the operating voltage of the on-chip memory can be reduced. However, this injects random bit errors, directly impacting the (quantized) DNN weights. As result, improving DNN robustness against these bit errors can significantly improve energy efficiency. Similarly, these chips are subject to bit-level hardware- or software-based attacks. In this case, robustness against adversarial bit errors is required to improve security of DNN accelerators. Our paper presented in this article addresses both problems.

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