Hey everyone,

I’ve been exploring running lightweight machine learning models (LLMS) on Android devices and came across the Neural Networks API, which seems promising for leveraging neural silicon ASICs.

I own a Google Pixel 7, which features an EdgeTPU integrated into the chip. I’m curious if anyone here has experience or success stories using this API for AI inference, particularly in comparison to traditional CPU performance.

My main interest lies in understanding the practical performance gains when using the EdgeTPU for AI tasks. Does it significantly outperform the CPU in terms of inference speed or efficiency?

I’m especially keen to hear from those who have experimented with similar setups or have insights into optimizing LLMS on Android devices using this technology.

Thanks in advance for your insights and experiences!

  • phree_radical@alien.topBEnglish
    1·
    1 year ago

    I dipped my toes in while comparing different methods of running Whisper on Android, and learned that they don’t intend developers to use NNAPI directly, but instead use a solution like TensorFlow Lite or PyTorch Mobile, which detects support and implements delegates which it may decide to use depending on the most efficient scenario. A developer needs to convert/“optimize” a model so that it doesn’t use any unsupported operations, but there’s also size considerations, like the TPU and other areas probably don’t have that much memory just yet