Search Results for author: Sangjung Woo

Found 3 papers, 2 papers with code

Toward Among-Device AI from On-Device AI with Stream Pipelines

1 code implementation16 Jan 2022 MyungJoo Ham, Sangjung Woo, Jaeyun Jung, Wook Song, Gichan Jang, Yongjoo Ahn, Hyoung Joo Ahn

We have started migrating the computing locations of intelligence services from cloud servers (traditional AI systems) to the corresponding devices (on-device AI systems).

NNStreamer: Efficient and Agile Development of On-Device AI Systems

no code implementations16 Jan 2021 MyungJoo Ham, Jijoong Moon, Geunsik Lim, Jaeyun Jung, Hyoungjoo Ahn, Wook Song, Sangjung Woo, Parichay Kapoor, Dongju Chae, Gichan Jang, Yongjoo Ahn, Jihoon Lee

NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal efforts.

NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications

1 code implementation12 Jan 2019 MyungJoo Ham, Ji Joong Moon, Geunsik Lim, Wook Song, Jaeyun Jung, Hyoungjoo Ahn, Sangjung Woo, Youngchul Cho, Jinhyuck Park, Sewon Oh, Hong-Seok Kim

We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications.

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