TensorFlow, an open source framework, is the most popular machine learning framework nowadays. TensorFlow Lite (TFLite), open sourced in late 2017, is TensorFlow’s runtime designed for mobile devices, esp. Android cell phones. As TFLite getting more and more mature, there are more and more interesting features, I’ll cover

1. delegates: including new NNAPI delegate, GPU delegate, and flex delegate,
2. optimized kernels for ARM CPUs,
3. various APIs: including Python, C, Objective-C, and Swift ones, and
4. misc, e.g., graph writer and Edge TPU.

I am contributor of TensorFlow, quite familiar with TensorFlow on edge devices. See my previous slide decks [1-6].

• [1] https://www.slideshare.net/kstan2/tensorflow-on-android
• [2] https://www.slideshare.net/kstan2/introduction-to-tensorflow-lite
• [3] https://www.slideshare.net/kstan2/caffe2-on-android
• [4] https://www.slideshare.net/kstan2/open-source-nn-frameworks-on-cellphones
• [5] https://www.slideshare.net/kstan2/why-you-cannot-use-neural-engine-to-run-your-nn-models-on-a11-devices
• [6] https://www.slideshare.net/kstan2/a-peek-into-googles-edge-tpu

Collaborative note: https://hackmd.io/@coscup/By9EgQl4H