Výsledky vyhledávání - "BOBARSHAD, HOSSEIN"

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In-storage Processing of I/O Intensive Applications on Computational Storage Drives

Autor: HeydariGorji, Ali, Torabzadehkashi, Mahdi, Rezaei, Siavash, Bobarshad, Hossein, Alves, Vladimir, Chou, Pai H.

Computational storage drives (CSD) are solid-state drives (SSD) empowered by general-purpose processors that can perform in-storage processing. They have the potential to improve both performance and energy significantly for big-data analytics by bri

Externí odkaz: http://arxiv.org/abs/2112.12415

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HyperTune: Dynamic Hyperparameter Tuning For Efficient Distribution of DNN Training Over Heterogeneous Systems

Autor: HeydariGorji, Ali, Rezaei, Siavash, Torabzadehkashi, Mahdi, Bobarshad, Hossein, Alves, Vladimir, Chou, Pai H.

Distributed training is a novel approach to accelerate Deep Neural Networks (DNN) training, but common training libraries fall short of addressing the distributed cases with heterogeneous processors or the cases where the processing nodes get interru

Externí odkaz: http://arxiv.org/abs/2007.08077

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STANNIS: Low-Power Acceleration of Deep Neural Network Training Using Computational Storage

Autor: HeydariGorji, Ali, Torabzadehkashi, Mahdi, Rezaei, Siavash, Bobarshad, Hossein, Alves, Vladimir, Chou, Pai H.

This paper proposes a framework for distributed, in-storage training of neural networks on clusters of computational storage devices. Such devices not only contain hardware accelerators but also eliminate data movement between the host and storage, r

Externí odkaz: http://arxiv.org/abs/2002.07215

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Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering

Autor: Bijari, Kayvan, Zare, Hadi, Veisi, Hadi, Bobarshad, Hossein

Publikováno v: Neural Comput & Applic (2016)

Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditi

Externí odkaz: http://arxiv.org/abs/1703.02883

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IEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection

Autor: Hajiabadi, Mahdi, Zare, Hadi, Bobarshad, Hossein

Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity structures, they suffer from either

Externí odkaz: http://arxiv.org/abs/1612.04679

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