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1,393 results on '"Nassar A"'

24. Effects of Runtime Reconfiguration on PUFs Implemented as FPGA-Based Accelerators.

Author

Nassar, Hassan, Bauer, Lars, and Henkel, Jorg

Abstract

Physical unclonable functions (PUFs) are a handy security primitive for resource-constrained devices. They offer an alternative to the resource-intensive classical hash algorithms. Using the IC differences resulting from the fabrication process, PUFs give device-specific outputs (responses) when given the same inputs (challenges). Hence, without using a device-specific key, PUFs can generate device-specific responses. FPGAs are one of the platforms that are heavily studied as a candidate for PUF implementation. The idea is that a PUF that is designed as an HDL code can be used as part of the static design or as a dynamic accelerator. Previous works studied PUF implementation as part of the static design. In contrast to the state-of-the-art, this letter studies PUFs when used as runtime reconfigurable accelerators. In this letter, we find that not all regions of an FPGA are equally suitable for implementing different PUF types. Regions, where clock routing resources exist, are the worst suited for PUF implementation. Moreover, we find out that for certain PUF types, the property of dynamic partial reconfiguration can lead to performance degradation if not applied carefully. When static routing passing through the region increases, the PUF performance degrades significantly. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Swift-CNN: Leveraging PCM Memory’s Fast Write Mode to Accelerate CNNs.

Author

Siddhu, Lokesh, Nassar, Hassan, Bauer, Lars, Hakert, Christian, Holscher, Nils, Chen, Jian-Jia, and Henkel, Joerg

Abstract

Nonvolatile memories [especially phase change memories (PCMs)] offer scalability and higher density. However, reduced write performance has limited their use as main memory. Researchers have explored using the fast write mode available in PCM to alleviate the challenges. The fast write mode offers lower write latency and energy consumption. However, the fast-written data are retained for a limited time and need to be refreshed. Prior works perform fast writes when the memory is busy and use slow writes to refresh the data during memory idle phases. Such policies do not consider the retention time requirement of a variable and repeat all the writes made during the busy phase. In this work, we suggest a retention-time-aware selection of write modes. As a case study, we use convolutional neural networks (CNNs) and present a novel algorithm, Swift-CNN, that assesses each CNN layer’s memory access behavior and retention time requirement and suggests an appropriate PCM write mode. Our results show that Swift-CNN decreases inference and training execution time and memory energy compared to state-of-the-art techniques and achieves execution time close to the ideal (fast write-only) policy. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Modeling and Experimental Determination of Lithium-Ion Battery Degradation in Hot Environment

Author

Almutairi, Z. A., primary, Eltamaly, Ali M, additional, Khereiji, A. El, additional, Nassar, A. Al, additional, Rished, A. Al, additional, Saheel, N. Al, additional, Marqabi, A. Al, additional, Hamad, S. Al, additional, Harbi, M. Al, additional, Sherif, R., additional, Almutairi, G., additional, Al-Amri, F., additional, and Hassanain, N. A., additional

Published
2022
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