Seyyed Salar Latifi Oskouei

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1ranked-venue papers

1as first author

0since 2021 · last 2016

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems 1 paper		Hardware accelerators and domain-specific architectures · 50% GPUs and heterogeneous computing · 50%
Artificial intelligence 1 paper		Efficient and distributed learning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Hardware accelerators and domain-specific architectures machine learning accelerator	0.2	1	2016	CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android · ACM Multimedia 2016
GPUs and heterogeneous computing › GPU computing › GPGPU acceleration mobile GPU acceleration	0.2	1	2016	CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android · ACM Multimedia 2016
Machine learning › Efficient and distributed learning › on-device inference mobile inference	0.1	1	2016	CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android · ACM Multimedia 2016

Year	Publication	Venue	Position
2016	CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android abstract Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid [1], for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https://github.com/ENCP/CNNdroid Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, Soheil Ghiasi	ACM Multimedia	1