  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.
