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arXiv 2022年

作者： Di Scala, Antonio J. Gangemi, Andrea Romeo, Giuliano Vernetti, Gabriele DISMA Department of Mathematical Sciences Politecnico of Turin Italy DAUIN Department of Control and Computer Engineering Politecnico of Turin Italy

In 2020 Sala, Sogiorno and Taufer [14] have been able to find the private keys of some Bitcoin addresses, thus being able to spend the cryptocurrency linked to them. This result was unexpected, since the recovery of non-trivial private keys for blockchain addresses is deemed to be an infeasible problem. In this paper we widen this analysis by mounting a similar attack to other small subsets of the set of private keys. We then apply it to other blockchains as well, examining Ethereum, Dogecoin, Litecoin, Dash, Zcash and Bitcoin Cash. In addition to the results, we also explain the techniques we have used to perform this exhaustive search for all the addresses that have ever appeared in these blockchains. © 2022, CC0.

关键词： Blockchain

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Soft Error Reliability Prediction of SRAM-based FPGA Designs

Soft Error Reliability Prediction of SRAM-based FPGA Designs

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European Conference on Radiation and its Effects on Components and Systems (RADECS)

作者： Eleonora Vacca Sarah Azimi Corrado De Sio Andrea Portaluri Daniele Rizzieri Luca Sterpone David Merodio Codinachs Christian Poivey Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy European Space Agency Noordwijk The Netherlands

We developed a tool for the reliability analysis of SEU effects on the configuration memory of Xilinx Zynq SRAM-based FPGAs. A proton radiation test campaign on different TMR layouts demonstrated the effectiveness of ...

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Analyzing the Reliability of Stream Sparse Matrix-Vector Multiplication Accelerators: A High-Level Approach

Analyzing the Reliability of Stream Sparse Matrix-Vector Mul...

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Latin American Test Workshop, LATW

作者： María L. Pinto-Salamanca Josie E. Rodriguez Condia José A. Hidalgo-López Wilson J. Pérez-Holguín GIRA Research Group Universidad Pedagógica y Tecnológica de Colombia (UPTC) Departamento de Electrónica Universidad de Málaga Department of Control and Computer Engineering (DAUIN) Politecnico di Torino

ISBN: (数字)9798350365559

ISBN: (纸本)9798350365566

1 Today, advances in scientific and embedded computing and the incredible proliferation of machine learning algorithms take advantage of specialized hardware accelerators to provide exceptional performance and good accuracy. In some safety-critical applications like autonomous robotics, healthcare, and automotive, crucial mathematical operations such as convolutions are often efficiently mapped in hardware as Matrix-Vector(MxV) and Matrix-Matrix (MxM) multiplications. Moreover, sophisticated sparsity algorithms aim to improve performance and power consumption. Unfortunately, technology scaling trends may increase the proliferation of faults on hardware during the in-field operation, so there is a rising interest in the reliability assessment and analysis of sparsity-based accelerators. This work evaluates the impact of soft errors (bit-flips) on sparse-matrix dense-vector multiplication (SpMV) cores for safety-critical tactile sensing applications by resorting to a High-Level Synthesis (HLS) strategy. The experiments are performed on an open-source streaming SpMV core using the Compressed Sparse Row (CSR) format when processing characteristic medium-size sparse matrices (100x100 and 494x494). Our results indicate that data-path pipeline registers in the (SpMV) core are resilient to transient faults (< 1.3% of observed corruption effects), while large magnitude errors can be associated with the type of sparse matrix describing the system (e.g., number of non-zero values) and the type and features of the input vector sensor.

关键词： Machine learning algorithms Power demand Pipelines Robot sensing systems Vectors Sensors Registers

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Reliability Assessment of Large DNN Models: Trading Off Performance and Accuracy

Reliability Assessment of Large DNN Models: Trading Off Perf...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Junchao Chen Giuseppe Esposito Fernando Fernandes dos Santos Juan-David Guerrero-Balaguera Angeliki Kritikakou Milos Krstic Robert Limas Sierra Josie E. Rodriguez Condia Matteo Sonza Reorda Marcello Traiola Alessandro Veronesi IHP - Leibniz Institute for High-Performance Microelectronics Germany Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Univ Rennes CNRS Inria IRISA - UMR 6074 Rennes France University of Potsdam Germany

ISBN: (数字)9798331539672

ISBN: (纸本)9798331539689

The adoption of Deep Neural Networks (DNNs) in several domains allows for increased effectiveness in applications that deal with massive data-intensive and complex data inputs. When employed in safety-critical scenarios, such as automotive, aerospace, healthcare, and autonomous robotics, assessing the DNNs' reliability and functional safety is crucial to ensure their correct in-field operation, even in the presence of hardware faults. However, the system complexity and the massive amounts of data to be processed by DNNs prevent the effective adoption of traditional strategies for reliability characterization and for identifying the most fault-sensitive structures. Accurate fault assessment strategies usually require unacceptable computational power and large evaluation times. On the other hand, faster strategies commonly lack accuracy in correctly representing system faults. Consequently, it is necessary to develop effective strategies that trade-off between performance and accuracy. This work analyses three reliability assessment strategies for deep neural networks and their underlying hardware, highlighting the main solutions and challenges in terms of evaluation performance and fault characterization accuracy. We overview different solutions to evaluate the hardware accelerators implementing DNNs at three abstraction levels: $i$ ) by physically injecting faults on a GPU running DNNs, ii) by performing microarchitectural characterization of GPUs to develop application-accurate error models, and iii) by using structure-aware cross-layer error modeling on DNN hardware accelerators. Our experimental results indicate that accurate error representation requires structural features from the targeted hardware.

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Characterizing a Neutron-Induced Fault Model for Deep Neural Networks

arXiv

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arXiv 2022年

作者： Dos Santos, Fernando Fernandes Kritikakou, Angeliki Condia, Josie E. Rodriguez Guerrero-Balaguera, Juan-David Reorda, Matteo Sonza Sentieys, Olivier Rech, Paolo University of Rennes Inria France Politecnico di Torino Department of Control and Computer Engineering Dauin Italy University of Trento Department of Industrial Engineering Italy

The reliability evaluation of Deep Neural Networks (DNNs) executed on Graphic Processing Units (GPUs) is a challenging problem since the hardware architecture is highly complex and the software frameworks are composed of many layers of abstraction. While software-level fault injection is a common and fast way to evaluate the reliability of complex applications, it may produce unrealistic results since it has limited access to the hardware resources and the adopted fault models may be too naive (i.e., single and double bit flip). Contrarily, physical fault injection with neutron beam provides realistic error rates but lacks fault propagation visibility. This paper proposes a characterization of the DNN fault model combining both neutron beam experiments and fault injection at software level. We exposed GPUs running General Matrix Multiplication (GEMM) and DNNs to beam neutrons to measure their error rate. On DNNs, we observe that the percentage of critical errors can be up to 61%, and show that ECC is ineffective in reducing critical errors. We then performed a complementary software-level fault injection, using fault models derived from RTL simulations. Our results show that by injecting complex fault models, the YOLOv3 misdetection rate is validated to be very close to the rate measured with beam experiments, which is 8.66× higher than the one measured with fault injection using only single-bit flips. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Digital Reconstruction and Storytelling: Exploring Funerary Artifacts from the Han Era in Immersive Virtual Reality

Digital Reconstruction and Storytelling: Exploring Funerary ...

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Lorenzo Valente Laura Vigo Francesca Ronco Marcello Raso Davide Calandra Roberta Spallone Fabrizio Lamberti Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Italy Musée des beaux-arts de Montréal Canada Department of Architecture and Design (DAD) Politecnico di Torino Italy Politecnico di Torino Italy

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This research focuses on the contextualization and digital reconstruction of twelve funerary artifacts from a Han Era archeological site, currently housed at the Asian Art Museum of Turin (MAO). The reconstructed artifacts have played a central role in a virtual exhibition that has integrated and represented the characteristic elements of Han art, emphasizing architecture, painting, and sculptural features, through the incorporation of storytelling. The process was developed through photogrammetric acquisition and 3D modelling of the funerary artifacts, reconstructive modeling and texturing of a Han tomb, along with a Virtual Reality (VR) experience enhanced by a set of informative descriptions. The study involved a multidisciplinary team of experts in Asian art, digital reconstruction, and information processing systems, with the support of MOD Lab Arch and VR@POLITO at the Politecnico di Torino.

关键词： Solid modeling Archeology Art Three-dimensional displays Accuracy Virtual reality Information processing Museums Photogrammetry Painting

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Physical and mental health of university staff during the Covid-19 pandemic

Physical and mental health of university staff during the Co...

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IEEE International Conference on Big Data

作者： Alessandra Colombelli Greta Temporin Francesco Serraino Tania Cerquitelli Department of Production and Management Engineering (DIGEP) Politecnico di Torino Turin Italy Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy

ISBN: (纸本)9781665480468

The 2020 Covid-19 pandemic caused a sudden and massive change in work organizations. One of the major consequences of the crisis was the acceleration towards teleworking, through the specific phenomenon of Mandatory Work From Home: the situations in which workers overnight found themselves to work seven days a week from their home environment, constantly online, often without adequate equipment and little to no preparation. Different workers reacted in different way to this important change, depending on age, gender, family characteristics and other impacting factors. Mandatory work from home and these other variables impacted employees’ physical and mental health, triggering or increasing symptoms of overwork and emotional exhaustion among others. This paper contributes to the literature on the impact of the pandemic on workers’ health by giving an overview of the effects of MWFH on university staff, using Politecnico di Torino as a case study.

关键词： COVID-19 Pediatrics Correlation Pandemics Teleworking Mental health Organizations

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Maneuver Coordination Using V2I to Improve Traffic Efficiency at Intersections

Maneuver Coordination Using V2I to Improve Traffic Efficienc...

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IEEE Vehicular Networking Conference (VNC)

作者： Lorenzo Farina Marco Rapelli Barbara Maví Masini Claudio Casetti Alessandro Bazzi DEI Università di Bologna Bologna Italy National Laboratory of Wireless Communications (WiLab) CNIT Bologna Italy Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Torino Italy National Research Council (CNR) - IEIIT Bologna Italy

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Traffic efficiency and air pollutants are major issues in urban transport systems, as they are critical points for traffic flows and people living in urban areas, respectively. In response to this challenge, connected and autonomous vehicles (CAV s) have garnered increasing attention, with the potential to address the above challenges through cooperative maneuvering and real-time communication. In this paper, we consider an intersection served by a multi-access edge computing (MEC) where CAVs communicate with a controller to pass the intersection without the need of signals and without stopping unless in presence of heavy congestion. For this purpose, we propose a first-in-first-scheduled (FIFS) algorithm, and we specify the communication protocol for its practical implementation. The solution is validated through simulations with varying traffic densities, showing that it provides significant gains in travel time and emissions compared to both uncontrolled intersections, with or without traffic lights, and a benchmark solution derived from the literature.

关键词： Protocols Multi-access edge computing Connected vehicles Roads Computational modeling Urban areas Benchmark testing

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IoT and Edge Computing: Applications and Reliability Implications

IoT and Edge Computing: Applications and Reliability Implica...

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Latin American Test Workshop, LATW

作者： Edward-W. Caro-Anzola Giuseppe Esposito Juan-David Guerrero-Balaguera Andrés-F. Jiménez-López Fabián-R. Jiménez-López Robert Limas-Sierra Gustavo Ramirez-Espinosa Edoardo Giusto Bartolomeo Montrucchio Oscar-F. Vera-Cely Josie E. Rodriguez Condia Universidad Pedagógica y Tecnológica de Colombia Faculty of Engineering Tunja Colombia Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Universidad de los Llanos Villavicencio Colombia Department of Electronics Pontificia Universidad Javeriana Bogotá Colombia University of Naples Federico II DIETI Naples Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Nowadays, the Internet of Things (IoT) and edge computing paradigms drive innovation in several fields, including autonomous systems, environmental monitoring, smart farming, and surveillance. The wide adoption of IoT and edge computing through commercial-off-the-shelf (COTS) provides adequate computational resources under limited energy consumption and affordable prices, which allow the development of versatile, scalable, and adaptable systems for several domains. Both features (versatility and scalability) have brought attention to domains with strict dependability requirements. However, ensuring effective, reliable, fault-tolerant IoT systems is challenging and mostly neglected in some domains. This special session briefly overviews strategies for the reliable design of IoT and edge computing systems for environmental monitoring in smart farming and urban scenarios. Then, we analyze an emerging paradigm (Split computing) and its reliability features. Further-more, the session discusses and focuses on design challenges, learned lessons, reliability implications, and open questions.

关键词： Smart agriculture Data integrity Reliability engineering Real-time systems Hardware Calibration Reliability Internet of Things Environmental monitoring Edge computing

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Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Cermelli, Fabio Mancini, Massimiliano Buló, Samuel Rota Ricci, Elisa Caputo, Barbara DAUIN Department of Control and Computer Engineering of Politecnico di Torino Turin Italy Cluster of Excellence"Machine Learning" University of Tübingen Germany Mapillary Research Graz Austria Italian Institute of Technology Turin Italy Fondazione Bruno Kessler Trento Italy Department of Information Engineering and Computer Science University of Trento Trento Italy

Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to incrementally update their model as new classes are available. Second, they rely on large amount of pixel-level annotations to produce accurate segmentation maps. To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift. Therefore, we revisit the traditional distillation paradigm by designing novel loss terms which explicitly account for the background shift. Additionally, we introduce a novel strategy to initialize classifier's parameters at each step in order to prevent biased predictions toward the background class. Finally, we demonstrate that our approach can be extended to point- and scribble-based weakly supervised segmentation, modeling the partial annotations to create priors for unlabeled pixels. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC, ADE20K, and Cityscapes datasets, significantly outperforming state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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