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of 997
pro vyhledávání: '"Daneshtalab A"'
Growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with respect to measuring the level of safety. Quantifying the reliability of emer
Externí odkaz:
http://arxiv.org/abs/2410.15742
Autor:
Mousavi, Seyedhamidreza, Ahmadilivani, Mohammad Hasan, Raik, Jaan, Jenihhin, Maksim, Daneshtalab, Masoud
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs against hardware faults, activation restriction techniques significantly
Externí odkaz:
http://arxiv.org/abs/2406.06313
Autor:
Ahmadilivani, Mohammad Hasan, Mousavi, Seyedhamidreza, Raik, Jaan, Daneshtalab, Masoud, Jenihhin, Maksim
Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault tolerance methods, such as Triple Modular Redundancy (TMR), are comput
Externí odkaz:
http://arxiv.org/abs/2405.10658
Autonomous driving systems are a rapidly evolving technology that enables driverless car production. Trajectory prediction is a critical component of autonomous driving systems, enabling cars to anticipate the movements of surrounding objects for saf
Externí odkaz:
http://arxiv.org/abs/2403.11695
Autor:
Taheri, Mahdi, Daneshtalab, Masoud, Raik, Jaan, Jenihhin, Maksim, Pappalardo, Salvatore, Jimenez, Paul, Deveautour, Bastien, Bosio, Alberto
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications. However, when used in safety-c
Externí odkaz:
http://arxiv.org/abs/2403.02946
Autor:
Taheri, Mahdi, Cherezova, Natalia, Nazari, Samira, Rafiq, Ahsan, Azarpeyvand, Ali, Ghasempouri, Tara, Daneshtalab, Masoud, Raik, Jaan, Jenihhin, Maksim
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
Externí odkaz:
http://arxiv.org/abs/2403.02936
Detecting lane markings in road scenes poses a challenge due to their intricate nature, which is susceptible to unfavorable conditions. While lane markings have strong shape priors, their visibility is easily compromised by lighting conditions, occlu
Externí odkaz:
http://arxiv.org/abs/2308.08242
Autor:
Ahmadilivani, Mohammad Hasan, Taheri, Mahdi, Raik, Jaan, Daneshtalab, Masoud, Jenihhin, Maksim
The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs
Externí odkaz:
http://arxiv.org/abs/2306.09973
Autor:
Ahmadilivani, Mohammad Hasan, Barbareschi, Mario, Barone, Salvatore, Bosio, Alberto, Daneshtalab, Masoud, Della Torca, Salvatore, Gavarini, Gabriele, Jenihhin, Maksim, Raik, Jaan, Ruospo, Annachiara, Sanchez, Ernesto, Taheri, Mahdi
Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a
Externí odkaz:
http://arxiv.org/abs/2306.04645
Autor:
Taheri, Mahdi, Ahmadilivani, Mohammad Hasan, Jenihhin, Maksim, Daneshtalab, Masoud, Raik, Jaan
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emulation in hardware are efficient and widely used to study the resilien
Externí odkaz:
http://arxiv.org/abs/2305.19733