Some smartphones nowadays pack laptop-level hardware — carrying up to 16GB RAM, high-speed multi-core CPUs, and GPUs that can render high-performance complex graphical applications on 4k displays.

Tapping into that power — especially the GPU processing power — for on-device data processing capabilities becomes growingly important as mobile hardware only continues to improve. Recently, this has been opening exciting opportunities around edge computing, federated architectures, mobile deep learning, and more.

This article provides a technical deep dive that shows you how to tap into the power of mobile cross-vendor GPUs. You will learn how to use the Android Native Development Kitand the Kompute framework to write GPU optimized code for Android devices. The end result will be a mobile app created in Android Studio that is able to use a GPU accelerated machine learning model which we will write from scratch, together with a user interface that will allow the user to send the input to the model.

Android Studio Running Project in Emulator (Image by Author)

No background knowledge beyond programming experience is required, however if you are curious about the underlying AI / GPU compute concepts referenced, we suggest checking out our previous article, “Machine Learning in Mobile & Cross-Vendor GPUs Made Simple With Kompute & Vulkan”.

You can find the full code in the example folder in the repository.

#artificial-intelligence #vulkan #machine-learning #android-ndk #mobile-apps