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- W4381573670 abstract "Deploying deep learning models on embedded devices is an arduous task: oftentimes, there exist no platform-specific instructions, and compilation times can be considerably large due to the limited computational resources available on-device. Moreover, many music-making applications demand real-time inference. Embedded hardware platforms for audio, such as Bela, offer an entry point for beginners into physical audio computing; however, the need for cross-compilation environments and low-level software development tools for deploying embedded deep learning models imposes high entry barriers on non-expert users. We present a pipeline for deploying neural networks in the Bela embedded hardware platform. In our pipeline, we include a tool to record a multichannel dataset of sensor signals. Additionally, we provide a dockerised cross-compilation environment for faster compilation. With this pipeline, we aim to provide a template for programmers and makers to prototype and experiment with neural networks for real-time embedded musical applications." @default.
- W4381573670 created "2023-06-22" @default.
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- W4381573670 date "2023-06-20" @default.
- W4381573670 modified "2023-10-03" @default.
- W4381573670 title "Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform" @default.
- W4381573670 doi "https://doi.org/10.48550/arxiv.2306.11389" @default.
- W4381573670 hasPublicationYear "2023" @default.
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