IAM

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Recorded MLSys’21 Talk “Bit Error Robustness for Energy-Efficient DNN Accelerators”

In this MLSys’21 paper, we consider the robustness of deep neural networks (DNN) against bit errors in their quantized weights. This is relevant in the context of DNN accelerators, i.e., specialized hardware for DNN inference: In order to reduce energy consumption, the accelerator’s memory may be operated at very low voltages. However, this induces exponentially increasing rates of bit errors that directly affect the DNN weights, reducing accuracy significantly. We propose a robust fixed-point quantization scheme, weight clipping as regularization during training and random bit error training to improve bit error robustness. This article shares my talk recorded for MLSys’21.

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Recorded RobustAI Workshop Talk “Confidence-Calibrated Adversarial Training and Bit Error Robustness of DNNs”

In January, I had the opportunity to interact with many other robustness researchers from academia and industry at the Robust Artificial Intelligence Workshop. As part of the workshop, organized by Airbus AI Research and TNO (Netherlands applied research organization), I also prepared a presentation talking about two of my PhD projects: confidence-calibrated adversarial training (CCAT) and bit error robustness of neural networks to enable low-energy neural network accelerators. In this article, I want to share the presentation; all other talks from the workshop can be found here.

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Recorded FOCA’20 Talk “Bit Error Robustness for Energy-Efficient DNN Accelerators”

In October this year, I was invited to talk at IBM’s FOCA workshop about my latest research on bit error robustness of (quantized) DNN weights. Here, the goal is to develop DNN accelerators capable to operating at low-voltage. However, lowering voltage induces bit errors in the accelerators’ memory. While such bit errors can be avoided through hardware mechanisms, such approaches are usually costly in terms of energy and area. Thus, training DNNs robust to such bit errors would enable low-voltage operation, reducing energy consumption, without the need for hardware techniques. In this 5-minute talk, I give a short overview.

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Recorded ICML’20 Talk “Confidence-Calibrated Adversarial Training”

In our ICML’20 paper, confidence-calibrated adversarial training (CCAT) addresses two problems of “regular” adversarial training. First, robustness against adversarial examples unseen during training is improved and second, clean accuracy is increased. CCAT biases the model towards predicting low-confidence on adversarial examples such that adversarial examples can be rejected by confidence thresholding. This article shares my talk on CCAT as recorded for ICML’20.

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Updated Pre-Print “Bit Error Robustness for Energy-Efficient DNN Accelerators “

Recently, deep neural network (DNN) accelerators have received considerable attention due to reduced cost and energy compared to mainstream GPUs. In order to further reduce energy consumption, the included memory (storing weights and intermediate computations) is operated at low voltage. However, this causes bit errors in memory cells, directly impacting the stored (quantized) DNN weights. This results in a significant decrease in CNN accuracy. In this paper, we tackle the problem of DNN robustness against random bit errors. By using a robust fixed-point quantization, training with aggressive weight clipping as regularization and injecting random bit errors during training, we increase robustness significantly, allowing energy-efficient DNN accelerators.

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Code Released: Adversarial Patch Training

The code for our paper on adversarial patch training on location-optimized adversarial patches is now available on GitHub. The repository includes a PyTorch implementation of our adversarial patch attack with location optimization as well as an adversarial training routine. The experiments on Cifar10 and GTSRB presented in the paper can easily be reproduced.

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What Lp Adversarial Examples make Sense on Common Vision Datasets?

Adversarial examples are intended to be imperceptible perturbations that cause mis-classification while not changing the true class. Still, there is no consensus on what changes are considered imperceptible or when the true class actually changes — or is not recognizable anymore. In this article, I want to explore what levels of $L_\infty$, $L_0$ and $L_1$ adversarial noise actually make sense on popular computer vision datasets such as MNIST, Fashion-MNIST, SVHN or Cifar10.

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ICML Talk “Confidence-Calibrated Adversarial Training”

Confidence-calibrated adversarial training (CCAT) addresses two problems when training on adversarial examples: the lack of robustness against adversarial examples unseen during training, and the reduced (clean) accuracy. In particular, CCAT biases the model towards predicting low-confidence on adversarial examples such that adversarial examples can be rejected by confidence thresholding. In this article, I want to share the slides of the corresponding ICML talk.

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ICML Paper “Confidence-Calibrated Adversarial Training”

Our paper on confidence-calibrated adversarial training was accepted at ICML’20. In the revised paper, the proposed confidence-calibrated adversarial training tackles the problem of obtaining robustness that generalizes to attacks not seen during training. This is achieved by biasing the network towards low-confidence predictions on adversarial examples and rejecting these low-confidence examples at test time. This article gives a short abstract and includes paper and code.

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ArXiv Pre-Print “On Mitigating Random and Adversarial Bit Errors”

Deep neural network (DNN) accelerators are specialized hardware for inference and have received considerable attention in the past years. Here, in order to reduce energy consumption, these accelerators are often operated at low voltage which causes the included accelerator memory to become unreliable. Additionally, recent work demonstrated attacks targeting individual bits in memory. The induced bit errors in both cases can cause significantly reduced accuracy of DNNs. In this paper, we tackle both random (due to low-voltage) and adversarial bit errors in DNNs. By explicitly taking such errors into account during training, wecan improve robustness significantly.

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