Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Raik, Jaan"'
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
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
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
Autor:
Ahmadilivani, Mohammad Hasan, Taheri, Mahdi, Raik, Jaan, Daneshtalab, Masoud, Jenihhin, Maksim
Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the last decade, rapid advances in ML have presented Deep
Externí odkaz:
http://arxiv.org/abs/2305.05750
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
Autor:
Taheri, Mahdi, Riazati, Mohammad, Ahmadilivani, Mohammad Hasan, Jenihhin, Maksim, Daneshtalab, Masoud, Raik, Jaan, Sjodin, Mikael, Lisper, Bjorn
While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the reliability of DNN accelerator
Externí odkaz:
http://arxiv.org/abs/2303.08226
Autor:
Ahmadilivani, Mohammad Hasan, Taheri, Mahdi, Raik, Jaan, Daneshtalab, Masoud, Jenihhin, Maksim
Publikováno v:
ETS 2023
Deep Neural Networks (DNNs) and their accelerators are being deployed ever more frequently in safety-critical applications leading to increasing reliability concerns. A traditional and accurate method for assessing DNNs' reliability has been resortin
Externí odkaz:
http://arxiv.org/abs/2303.06931