1,720,960 research outputs found
SCI-FI: a Smart, aCcurate and unIntrusive Fault-Injector for Deep Neural Networks
No full textIn recent years, the reliability of Deep Neural Networks (DNN) has become the focus of an increasing number of research activities. In particular, researchers have focused on understanding how a DNN behaves when the underlying hardware is affected by a fault. This is a challenging task: slight changes in a network architecture can significantly impact how the network reacts to faults. There are several approaches to simulate the behaviour of a faulty network: the most accurate one is to perform low-level fault simulations. Nonetheless, this task is very time-consuming and costly to be implemented. Even though the injection time can be reduced by injecting faults at the application level, for sufficiently large networks, this time is still very high, requiring weeks to complete a single simulation. This work aims at providing a fast and accurate solution for injecting software-level faults in a DNN that is independent of its architecture and does not require any modification to its structure. For this reason, this paper introduces SCI-FI, a Smart, aCcurate and unIntrusive Fault-Injector. SCI-FI smartly reduces the fault injection time required for a complete fault simulation of the network by taking advantage of two fundamental mechanisms: Fault Dropping and Delayed Start. Experimental results from various ResNet, DenseNet and EfficientNet architectures targeting the CIFAR-10 and ImageNet datasets show that combining these techniques drastically reduces the simulation time, which can last up to 70% less
On the resilience of representative and novel data formats in CNNs
No full textIn recent years, a wide range of data type representations have been employed for training and storing the parameters of Deep Neural Networks (DNNs). The decision to employ a particular data type over another is influenced by various requirements, including the desire to enhance training accuracy or reduce data size to minimize memory usage, energy and power consumption. However, opting for one data type over another inevitably impacts the reliability of the model. This work studies the impact of different data representations on the reliability of LeNet-5, a popular Convolutional Neural Network (CNN) used for image classification tasks.An investigation is performed to evaluate the efficacy of the Average Bit-Flip Distance (ABFD) in predicting the criticality of bit positions in the data representation. The data type under analysis are FP32, POSIT32, POSIT16 and INT8. Together with the widely adopted metrics, this work proposes a new metric, called Soft SDC-n, to measure the percentage of faults that cause a change in the order of the top-n output elements. Experimental results shows that POSIT is not as reliable as FP32, while indicating that the most reliable data type is INT8. Furthermore, the same results confirm the presence of a relationship between the ABFD and the criticality of a bit in all the data representations under analysis
A Fast Reliability Analysis of Image Segmentation Neural Networks Exploiting Statistical Fault Injections
No full textThe reliability of hardware running deep neural networks (DNNs) is becoming the object of multiple research works. Fault injections (FIs) are one of the most used solutions to determine the reliability of DNN models. However, defining how many faults to inject in the model is not a trivial task. An exhaustive FI campaign requires injecting, in modern DNNs, billions or trillions of parameters. On the other hand, random FI campaigns do not offer a practical measure of the accuracy of the result. A different approach is to perform a statistical FI: the number of faults to inject is decided based on the number of possible faults and by fixing an error margin and a confidence level on the measured output metric. While the statistical approach offers the best of both worlds, it requires a proper setup to guarantee its statistically significance. In this work, a study on the statistical fault injection procedure on an image segmentation neural network is proposed. In particular, the study compares results from a random FI campaign and an improperly-defined statistical FI campaign, and shows how they fail at highlighting some of the critical aspects of U-Net, a state-of-the-art DNN used for image segmentation. The proposed approach, by injecting only the 0.07% of all the possible faults, accurately measures both the criticality of each layer and of the parameters' bit with an error margin of 1% and a confidence level of 99%
Open-Set Recognition: an Inexpensive Strategy to Increase DNN Reliability
No full textDeep Neural Networks (DNNs) are nowadays widely used in low-cost accelerators, characterized by limited computational resources. These models, and in particular DNNs for image classification, are becoming increasingly popular in safety-critical applications, where they are required to be highly reliable. Unfortunately, increasing DNNs reliability without computational overheads, which might not be affordable in low-power devices, is a non-trivial task. Our intuition is to detect network executions affected by faults as outliers with respect to the distribution of normal network's output. To this purpose, we propose to exploit Open-Set Recognition (OSR) techniques to perform Fault Detection in an extremely low-cost manner. In particuar, we analyze the Maximum Logit Score (MLS), which is an established Open-Set Recognition technique, and compare it against other well-known OSR methods, namely OpenMax, energy-based outof-distribution detection and ODIN. Our experiments, performed on a ResNet-20 classifier trained on CIFAR-10 and SVHN datasets, demonstrate that MLS guarantees satisfactory detection performance while adding a negligible computational overhead. Most remarkably, MLS is extremely convenient to conFigure and deploy, as it does not require any modification or re-training of the existing network. A discussion of the advantages and limitations of the analysed solutions concludes the paper
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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