VLDB 2026 Research / reviewers in the wild / expert

Parsa Esfahanian

dblp:241/4369 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · unresolved

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

Systems, architecture and hardware · 1

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 · 61% Emerging computing paradigms · 30% Embedded and real-time systems · 9%

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

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures › machine learning accelerator
neural network accelerator		 0.412019
SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks · DAC 2019
Emerging computing paradigms › approximate and stochastic computing
stochastic computing		 0.412019
SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks · DAC 2019
Hardware accelerators and domain-specific architectures › machine learning accelerator › neural network accelerator
stochastic computing accelerator		 0.412019
SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks · DAC 2019
Embedded and real-time systems
embedded machine learning		 0.112019
SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks · DAC 2019

Methods — techniques the papers use, named apart from their topics

stochastic computing arithmetic · 0.4
YearPublicationVenuePosition
2019 SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks
abstract
Employing convolutional neural networks (CNNs) in embedded devices seeks novel low-cost and energy efficient CNN accelerators. Stochastic computing (SC) is a promising low-cost alternative to conventional binary implementations of CNNs. Despite the low-cost advantage, SC-based arithmetic units suffer from prohibitive execution time due to processing long bit-streams. In particular, multiplication as the main operation in convolution computation, is an extremely time-consuming operation which hampers employing SC methods in designing embedded CNNs.
Reza Hojabr, Kamyar Givaki, S. M. Reza Tayaranian, Parsa Esfahanian, Ahmad Khonsari, Dara Rahmati, M. Hassan Najafi
 DAC4