4 citations · 8 across the 5 of their papers we have counts for
5 papers
Toward Among-Device AI from On-Device AI with Stream Pipelines
MyungJoo Ham, Sangjung Woo, Jaeyun Jung +4
Modern consumer electronic devices often provide intelligence services with deep neural networks. We have started migrating the computing locations of intelligence services from cl…
LightSys: Lightweight and Efficient CI System for Improving Integration Speed of Software
Geunsik Lim, MyungJoo Ham, Jijoong Moon +1
The complexity and size increase of software has extended the delay for developers as they wait for code analysis and code merge. With the larger and more complex software, more de…
TAOS-CI: Lightweight & Modular Continuous Integration System for Edge Computing
Geunsik Lim, MyungJoo Ham, Jijoong Moon +3
With the proliferation of IoT and edge devices, we are observing a lot of consumer electronics becoming yet another IoT and edge devices. Unlike traditional smart devices, such as…
NNStreamer: Efficient and Agile Development of On-Device AI Systems
MyungJoo Ham, Jijoong Moon, Geunsik Lim +9
We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A…
NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications
MyungJoo Ham, Ji Joong Moon, Geunsik Lim +8
We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications. A new…