VLDB 2026 Research / reviewers in the wild / expert

Guy Boudoukh

dblp:69/8109 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Systems, architecture and hardware · 1Graphics, 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 · 70% Reconfigurable computing and FPGAs · 23% GPUs and heterogeneous computing · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures › machine learning accelerator
DNN acceleration		 0.312017
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? · FPGA 2017
Hardware accelerators and domain-specific architectures › machine learning accelerator
DNN accelerator		 0.312017
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? · FPGA 2017
Reconfigurable computing and FPGAs
FPGA accelerator		 0.312017
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? · FPGA 2017
Hardware accelerators and domain-specific architectures
machine learning accelerator		 0.312017
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? · FPGA 2017
GPUs and heterogeneous computing
GPU computing		 0.112017
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? · FPGA 2017

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

performance comparison · 0.3
YearPublicationVenuePosition
2017 Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Eriko Nurvitadhi, Ganesh Venkatesh, Jaewoong Sim, Debbie Marr, Randy Huang, Jason Ong Gee Hock, Yeong Tat Liew, Krishnan Srivatsan, Duncan J. M. Moss, Suchit Subhaschandra, Guy Boudoukh
 FPGA11
2009 Visual tracking of object silhouettes
abstract
In this paper we propose a new method that addresses the problem of tracking the bitmap (silhouette) of an object in a video under very general conditions. We assume a general target, possibly non rigid, with no prior information except initialization. The target, as well as the background, may change its appearance over time and the camera may move arbitrarily. The proposed algorithm fuses different visual cues by means of a conditional random field. The target's bitmap is estimated every frame by incorporating temporal color similarity, spatial color continuity and spatial motion continuity into an energy function that is minimized via min-cut. The spatial motion continuity is incorporated in the energy function in multiple image resolutions by a novel multi-scale energy term. Experiments demonstrate the robustness of our method and its advantage over other algorithms.
Guy Boudoukh, Ido Leichter, Ehud Rivlin
 ICIP1