Charbel Sakr

428 citations

15 papers · 249 indexed · h-index 12

Co-authors: Naresh R. Shanbhag Yongjune Kim Sujan K. Gonugondla Yingyan Lin Brian Zimmer Pavlo Molchanov Michael B. Sullivan Timothy Tsai
Topics: Advanced Neural Network Applications (8 papers)Advanced Memory and Neural Computing (5 papers)Ferroelectric and Negative Capacitance Devices (5 papers)
Cited by: Computer Vision and Pattern Recognition Hardware and Architecture Artificial Intelligence
Journals: IEEE Transactions on Signal Processing IEEE Journal of Solid-State Circuits Soft Matter
Partner nations: United States Luxembourg France

In The Last Decade

Charbel Sakr

14 papers receiving 246 citations

Peers

Countries citing papers authored by Charbel Sakr

Since Specialization

Citations

This map shows the geographic impact of Charbel Sakr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charbel Sakr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charbel Sakr more than expected).

Fields of papers citing papers by Charbel Sakr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charbel Sakr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charbel Sakr. The network helps show where Charbel Sakr may publish in the future.

Co-authorship network of co-authors of Charbel Sakr

This figure shows the co-authorship network connecting the top 25 collaborators of Charbel Sakr. A scholar is included among the top collaborators of Charbel Sakr based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charbel Sakr. Charbel Sakr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm 2023 ·IEEE Journal of Solid-State Circuits ·Ben Keller,Rangharajan Venkatesan,Steve Dai,Stephen G. Tell,Brian Zimmer,Charbel Sakr,William J. Dally,C. Thomas Gray,Brucek Khailany	30
2	VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning 2023 ·(unknown),Siva Kumar Sastry Hari,Balakumar Sundaralingam,(unknown),Thierry Tambe,Charbel Sakr,Stephen W. Keckler,Vijay Janapa Reddi	2
3	Unique orientation of 1D and 2D nanoparticle assemblies confined in smectic topological defects 2022 ·Soft Matter ·(unknown),(unknown),Charbel Sakr,(unknown),(unknown),Alina Vlad,Alessandro Coati,Nicolas Goubet,Sébastien Royer,I. Vickridge,Michel Goldmann,Doru Constantin,Y. Garreau,David Babonneau,B. Croset,Bruno Gallas,Emmanuel Lhuillier,Emmanuelle Lacaze	11
4	Optimizing Selective Protection for CNN Resilience 2021 ·Abdulrahman Mahmoud,Siva Kumar Sastry Hari,Christopher W. Fletcher,Sarita V. Adve,Charbel Sakr,Naresh R. Shanbhag,Pavlo Molchanov,Michael B. Sullivan,Timothy Tsai,Stephen W. Keckler	26
5	Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures 2021 ·IEEE Transactions on Signal Processing ·Charbel Sakr,Naresh R. Shanbhag	14
6	KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting 2020 ·(unknown),Sujan K. Gonugondla,Charbel Sakr,Naresh R. Shanbhag	11
7	A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting 2020 ·IEEE Journal of Solid-State Circuits ·(unknown),Sujan K. Gonugondla,Charbel Sakr,Naresh R. Shanbhag	19
8	Fundamental limits on the precision of in-memory architectures 2020 ·Sujan K. Gonugondla,Charbel Sakr,(unknown),Naresh R. Shanbhag	15
9	Minimum Precision Requirements of General Margin Hyperplane Classifiers 2019 ·IEEE Journal on Emerging and Selected Topics in Circuits and Systems ·Charbel Sakr,Yongjune Kim,Naresh R. Shanbhag	1
10	An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks 2018 ·Charbel Sakr,Naresh R. Shanbhag	22
11	True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization 2018 ·Charbel Sakr,Jungwook Choi,Zhuo Wang,Kailash Gopalakrishnan,Naresh R. Shanbhag	12
12	Minimum Precision Requirements for Deep Learning with Biomedical Datasets 2018 ·Charbel Sakr,Naresh R. Shanbhag	1
13	Analytical guarantees on numerical precision of deep neural networks 2017 ·Open Access System for Information Sharing (Pohang University of Science and Technology) ·Charbel Sakr,Yongjune Kim,Naresh R. Shanbhag	38
14	Minimum precision requirements for the SVM-SGD learning algorithm 2017 ·Charbel Sakr,Ameya D. Patil,(unknown),Yongjune Kim,Naresh R. Shanbhag	12
15	PredictiveNet: An energy-efficient convolutional neural network via zero prediction 2017 ·Yingyan Lin,Charbel Sakr,Yongjune Kim,Naresh R. Shanbhag	35

About Charbel Sakr

Charbel Sakr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture, having authored 15 papers that have together received 249 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (8 papers), Advanced Memory and Neural Computing (5 papers) and Ferroelectric and Negative Capacitance Devices (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (102 citations), Hardware and Architecture (28 citations) and Artificial Intelligence (109 citations). Charbel Sakr has collaborated with scholars based in United States, Luxembourg and France. Frequent co-authors include Naresh R. Shanbhag, Yongjune Kim, Sujan K. Gonugondla, Yingyan Lin, Brian Zimmer, Pavlo Molchanov, Michael B. Sullivan, Timothy Tsai, Christopher W. Fletcher and Brucek Khailany. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Journal of Solid-State Circuits and Soft Matter.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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