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Interpreter: Executes the model using a set of operators.

The same library is available on both Android and iOS

C++ API: Loads the TensorFlow Lite Model File and invokes.

Java API: A convenience wrapper around the C++ API on.

The TensorFlow Lite Model File is then deployed within a Mobile App, where: Optimized for maximum speed and minimum size.

TensorFlow Lite Model File: A model file format based on FlatBuffers, that has been.

Model to the TensorFlow Lite file format.

TensorFlow Lite Converter: A program that converts the.

TensorFlow Model: A trained TensorFlow model saved on disk.

The following diagram shows the architectural design of TensorFlow Lite:

Is not available, which ensures your models can still run fast on a large set of TensorFlow Lite falls back to optimized CPU execution when accelerator hardware Neural Networks API to take advantage of these new accelerators as they come

More and more mobile devices today incorporate purpose-built custom hardware to Improved model loading times, and supporting hardware acceleration Optimized for mobile devices, including dramatically Models with a small binary size and fast initialization/startupĪ runtime designed to run on many different TensorFlow Lite enables low-latency inference of on-device machine learning models.Įnables inference of on-device machine learning Today, we're happy to announce the developer preview of TensorFlow Lite, TensorFlow’s lightweight solution for mobile and embedded devices! TensorFlow has always run on many platforms, from racks of servers to tiny IoT devices, but as the adoption of machine learning models has grown exponentially over the last few years, so has the need to deploy them on mobile and embedded devices.