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Jaeho Jin

dblp:392/9974 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved

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

Human-computer interaction and ubiquitous computing · 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
Hardware accelerators and domain-specific architectures · 50% GPUs and heterogeneous computing · 25% Embedded and real-time systems · 25%
Computer graphics and multimedia
1 paper
Rendering · 100%

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 inference
DNN inference scheduling
1.012026
Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs · PerCom 2026
Hardware accelerators and domain-specific architectures
machine learning accelerator
1.012026
Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs · PerCom 2026
GPUs and heterogeneous computing › embedded GPU
mobile GPU
1.012026
Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs · PerCom 2026
Embedded and real-time systems › real-time scheduling
preemptive scheduling
1.012026
Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs · PerCom 2026
Rendering
real-time rendering
0.312026
Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs · PerCom 2026

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

adaptive operator chunking · 2.0GPU queue latency probing · 2.0
YearPublicationVenuePosition
2026 Foreground Graphics-Aware Runtime DNN Scheduling on Mobile GPUs
abstract
We propose GPUSched, a foreground graphics aware preemptive scheduling framework for concurrent DNN inference and seamless graphics rendering on mobile GPUs. Given that existing GPU scheduling methods relying on offline slicing are inadequate for dynamic graphics workloads, GPUSched is driven by two key components: (i) Render-State Detection via GPUPing, which leverages lightweight probing of GPU queue latency to accurately identify foreground rendering operations and (ii) Adaptive Operator Chunking, which dynamically fits DNN model chunk sizes within GPU idle times in a GPU frequency-aware manner. By combining these mechanisms GPUSched, coordinates DNN inference with graphics rendering in real time, preventing deadline violations while maximizing GPU utilization. Experiments on two commodity mobile devices show that GPUSched consistently outperforms baseline schedulers, achieving lower deadline miss rates while sustaining high rendering quality even under demanding graphics workload.
Minju Kang, Jaeho Jin, JeongGil Ko
PerCom2