4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2022
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…
cs.LG2021★ 1 cited
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…
cs.DC2019★ 4 cited
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…