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    A case for PIM support in general-purpose compilers

    , Article IEEE Design and Test ; 2021 ; 21682356 (ISSN) Sadeghi, P ; Ejlali, A ; Sharif University of Technology
    IEEE Computer Society  2021
    Abstract
    Newly developed 3D die stacking technologies affords us the possibility to revisit the idea of Processing-in-Memory (PIM) as implementation hurdles are overcome. We now have the opportunity to offload the data intensive parts of our program to the PIM in form of kernels to be able to take advantage of the high internal bandwidth of the memory modules. Memory access latency and bandwidth are two major bottlenecks in today’s high-performance computers and new use-cases are moving faster than ever before towards this mode of computing. With new graph processing and neural network applications being developed every day, having a performance model of such systems helps in predicting the behavior... 

    A case for PIM support in general-purpose compilers

    , Article IEEE Design and Test ; Volume 39, Issue 2 , 2022 , Pages 84-89 ; 21682356 (ISSN) Sadeghi, P ; Ejlali, A ; Sharif University of Technology
    IEEE Computer Society  2022
    Abstract
    This work presents a case for general support for processing-in-memory (PIM) in compilers and puts forth an approach to face it along with a simple model. The ultimate goal of the work is to implement the features in a general-purpose compiler that can compile for any homogeneous ISA system, so the benefits from PIM are not limited to niche use-cases. © 2013 IEEE  

    Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    , Article 4th Global Conference on Materials Science and Engineering, CMSE 2015, 3 August 2015 through 6 August 2015 ; Volume 103, Issue 1 , December , 2015 ; 17578981 (ISSN) Maleki, E ; Ashton A ; Ruda H. E ; Khotsianovsky A ; Sharif University of Technology
    Institute of Physics Publishing  2015
    Abstract
    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI)... 

    A Fast vacuum arc detection method based on the neural network data fusion algorithm for the high-voltage dc power supply of vacuum tubes

    , Article IEEE Transactions on Plasma Science ; 2020 Ayoubi, R ; Kaboli, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    Vacuum arc is one of the most important failure factors of the vacuum tubes. The amount of delivered energy from the high-voltage dc power supply to the vacuum tube is an important issue during the vacuum arc in the tube. Vacuum arc acts as a short-circuit fault (SCF) at the power supply output. The majority of converters use a single current sensor to measure only the converter output current for detecting the SCF. However, the sensor may provide unreliable data because of the noise effect. Application of a low-pass filter reduces the noise effect. Regarding the delay of the low-pass filter, the interval of arc detection increases and more energy is delivered to the tube. In this article, a... 

    A content-based deep intrusion detection system

    , Article International Journal of Information Security ; Volume 21, Issue 3 , 2022 , Pages 547-562 ; 16155262 (ISSN) Soltani, M ; Siavoshani, M. J ; Jahangir, A. H ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2022
    Abstract
    The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, and leading to an increase in cyber threats and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there are many studies on using learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks...