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Yuichiro Kikura

dblp:207/2022 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
1 paper
Efficient and distributed learning · 100%
Software engineering, system software, and programming languages
1 paper
Runtime systems and virtual machines · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
model compression
0.312017
WebDNN: Fastest DNN Execution Framework on Web Browser · ACM Multimedia 2017
Runtime systems and virtual machines › dynamic language implementation
javascript runtime
0.312017
WebDNN: Fastest DNN Execution Framework on Web Browser · ACM Multimedia 2017

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

model optimization · 0.6GPGPU · 0.6
YearPublicationVenuePosition
2017 WebDNN: Fastest DNN Execution Framework on Web Browser
abstract
Recently, deep neural network (DNN) is drawing a lot of attention because of its applications. However, it requires a lot of computational resources and tremendous processes in order to setup an execution environment based on hardware acceleration such as GPGPU. Therefore, providing DNN applications to end-users is very hard. To solve this problem, we have developed an installation-free web browser-based DNN execution framework, WebDNN. WebDNN optimizes the trained DNN model to compress model data and accelerate the execution. It executes the DNN model with novel JavaScript API to achieve zero-overhead execution. Empirical evaluations show that it achieves more than two-hundred times the unusual acceleration. WebDNN is an open source framework and you can download it from https://github.com/mil-tokyo/webdnn.
Masatoshi Hidaka, Yuichiro Kikura, Yoshitaka Ushiku, Tatsuya Harada
ACM Multimedia2