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Changjin Jeong

dblp:322/6784 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Computer networks · 1 · 1 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
GPUs and heterogeneous computing · 87% Embedded and real-time systems · 13%

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

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › heterogeneous architecture
heterogeneous mobile processors		 0.612022
Band: coordinated multi-DNN inference on heterogeneous mobile processors · MobiSys 2022
GPUs and heterogeneous computing › deep learning on GPUs
multi-DNN inference		 0.612022
Band: coordinated multi-DNN inference on heterogeneous mobile processors · MobiSys 2022
Embedded and real-time systems › on-device inference
mobile inference		 0.212022
Band: coordinated multi-DNN inference on heterogeneous mobile processors · MobiSys 2022

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

operator partitioning · 0.6coordinated scheduling · 0.6
YearPublicationVenuePosition
2022 Band: coordinated multi-DNN inference on heterogeneous mobile processors
abstract
The rapid development of deep learning algorithms, as well as innovative hardware advancements, encourages multi-DNN workloads such as augmented reality applications. However, existing mobile inference frameworks like TensorFlow Lite and MNN fail to efficiently utilize heterogeneous processors available on mobile platforms, because they focus on running a single DNN on a specific processor. As mobile processors are too resource-limited to deliver reasonable performance for such workloads by their own, it is challenging to serve multi-DNN workloads with existing frameworks.
Joo Seong Jeong, Jingyu Lee, Changmin Jeon, Changjin Jeong, Youngki Lee 0001, Byung-Gon Chun
 MobiSys5