Sahand Divsalar

dblp:415/9953 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0002-9181-4300ORCID · corroborated

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

Systems, architecture and hardware · 3 · 1 first-author · 3 since 2021

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
Emerging computing paradigms · 61% Reconfigurable computing and FPGAs · 30% Hardware accelerators and domain-specific architectures · 9%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms
approximate computing
1.012026
LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026
Emerging computing paradigms › approximate computing
approximate multiplier
1.012026
LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026
Hardware accelerators and domain-specific architectures › machine learning accelerator › neural network accelerator
energy-efficient neural network accelerator
0.312026
LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026
YearPublicationVenuePosition
2026 ARTS: An approximate reduced tree and segmentation-based multiplier
Mahla Salehi Sheikhali Kelayeh, Sahand Divsalar, Shaghayegh Vahdat, Nima Taherinejad
Future Gener. Comput. Syst.2
2026 LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications
Mahla Salehi Sheikhali Kelayeh, Sahand Divsalar, Shaghayegh Vahdat, Nima Taherinejad
IEEE Trans. Computers2
2026 On the use of approximate computing for improving the robustness of DNNs against adversarial attacks
Sahand Divsalar, Fatemeh Arezoomand, Shaghayegh Vahdat, Ali Afzali-Kusha, Massoud Pedram
J. Supercomput.1