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We propose NNStreamer, a software system that handles neural networks asfilters of stream pipelines, applying the stream processing paradigm to deepneural network applications. A new trend with the wide-spread of deep neuralnetwork applications is on-device AI. It is to process neural networks onmobile devices or edge/IoT devices instead of cloud servers. Emerging privacyissues, data transmission costs, and operational costs signify the need foron-device AI, especially if we deploy a massive number of devices. NNStreamerefficiently handles neural networks with complex data stream pipelines ondevices, significantly improving the overall performance with minimal efforts.Besides, NNStreamer simplifies implementations and allows reusing off-the-shelfmedia filters directly, which reduces developmental costs significantly. We arealready deploying NNStreamer for a wide range of products and platforms,including the Galaxy series and various consumer electronic devices. Theexperimental results suggest a reduction in developmental costs and enhancedperformance of pipeline architectures and NNStreamer. It is an open-sourceproject incubated by Linux Foundation AI, available to the public andapplicable to various hardware and software platforms.

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