A New Frontier of AI: On-Device AI Training and Personalization

1 code implementation • 9 Jun 2022 • Ji Joong Moon, Hyun Suk Lee, Jiho Chu, Donghak Park, Seungbaek Hong, Hyungjun Seo, Donghyeon Jeong, Sungsik Kong, MyungJoo Ham

Modern consumer electronic devices have started executing deep learning-based intelligence services on devices, not cloud servers, to keep personal data on devices and to reduce network and cloud costs.

Efficient Neural Network speech-recognition +1

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

1 code implementation • 16 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 implementations • 16 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 implementation • 12 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.