Pneumonia is a prevalent respiratory infection with potentially life-threatening consequences. In this research, we propose a novel deep learning approach to enhance pneumonia detection using the Vision Transformer (V...
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Induction motors carry notable importance in modern machinery and industrial equipment. Hence, the imperative to establish an early fault detection system for discerning operational states and potential faults in thes...
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Leakage assessment at the Register Transfer Level (RTL) is essential for identifying vulnerabilities in various designs, including cryptographic systems, AI models, and other applications handling sensitive data durin...
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ISBN:
(数字)9781665477635
ISBN:
(纸本)9781665477642
Leakage assessment at the Register Transfer Level (RTL) is essential for identifying vulnerabilities in various designs, including cryptographic systems, AI models, and other applications handling sensitive data during the design phase. This paper introduces VeriSide, an innovative framework built as a modified version of Verilator to generate compact format files that directly capture side-oriented information, such as Hamming Distance (HD) or Hamming Weight (HW) of the signals. VeriSide streamlines the power side-channel (PSC) analysis process by providing efficient and scalable solutions for large-scale designs. Traditional methods relying on verbose Value Change Dump (VCD) or Switching Activity Interchange Format (SAIF) files face significant scalability and resource challenges, especially for complex systems-on-chip (SoCs). These methods incur substantial storage and processing overheads. VeriSide overcomes these limitations by drastically reducing file size and eliminating post-simulation memory usage, while maintaining analysis accuracy.
1 This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in...
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ISBN:
(数字)9798350349320
ISBN:
(纸本)9798350349337
1
This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in the cores for two operations (Add, and Multiply), which are the most used in several modern applications, including machine learning. The results show that Posit cores are structurally more vulnerable to fault propagation and induce more output corruptions than FP cores (from 3.3% up to 6.2%). Moreover, we found that the average absolute error in faulty FP cores is higher by up to 2 orders of magnitude than in Posit ones.
Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years, exposing the hardware to aging (i.e., permanent faults arising after the end-of-manufacturing test). Hence, techniques to assess p...
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The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electr...
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Interacting with real-world cluttered scenes poses several challenges to robotic agents that need to understand complex spatial dependencies among the observed objects to determine optimal pick sequences or efficient ...
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Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we...
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ISBN:
(数字)9798331508050
ISBN:
(纸本)9798331508067
Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we propose a novel approach for generating on-demand datasets using the Simulator of Urban Mobility (SUMO) and a Generative Adversarial Network (GAN). Our method focuses on capturing critical events such as sudden pedestrian crossings, near-misses, and collisions, providing essential data to improve vehicle models' responses to emergency situations.
The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small...
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