Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Taheri Mahdi"'
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
Publikováno v:
Journal of Horticultural Research, Vol 25, Iss 1, Pp 99-115 (2017)
Two Allium species (A. akaka S.G. Gmelin and A. elburzense W.) native to Iran are used locally as the fresh vegetables and in medical therapy. They are not cultivated, but are collected from the wild, thus, will soon be threatened with extinction. In
Externí odkaz:
https://doaj.org/article/343c3c615e1b4d748f17bfa00a69370e
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
We introduce a novel logic style with self-checking capability to enhance hardware reliability at logic level. The proposed logic cells have two-rail inputs/outputs, and the functionality for each rail of outputs enables construction of faulttolerant
Externí odkaz:
http://arxiv.org/abs/2306.00844
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
The human face contains important and understandable information such as personal identity, gender, age, and ethnicity. In recent years, a person's age has been studied as one of the important features of the face. The age estimation system consists
Externí odkaz:
http://arxiv.org/abs/2305.00848
The first important step in extracting DNA characters is using the output data of MinION devices in the form of electrical current signals. Various cutting-edge base callers use this data to detect the DNA characters based on the input. In this paper
Externí odkaz:
http://arxiv.org/abs/2303.08915