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TensorFlow for Mobile and IoT and TensorFlow.js
Supported platforms
On Android, TensorFlow Lite inference can be performed using either Java or C++.
On iOS, TensorFlow Lite inference can run in Swift and Objective-C. On Linux
platforms (such as Raspberry Pi), inferences run in C++ and Python. TensorFlow
Lite for microcontrollers is an experimental port of TensorFlow Lite designed
to run machine learning models on microcontrollers based on Arm Cortex-M
(https://developer.arm.com/ip-products/processors/cortex-m) Series
processors including Arduino Nano 33 BLE Sense (https://store.arduino.
cc/usa/nano-33-ble-sense-with-headers), SparkFun Edge (https://www.
sparkfun.com/products/15170), and the STM32F746 Discovery kit (https://www.
st.com/en/evaluation-tools/32f746gdiscovery.html). These microcontrollers
are frequently used for IoT applications.
Architecture
The architecture of TensorFlow Lite is described in Figure 2 (from https://
www.tensorflow.org/lite/convert/index). As you can see, both tf.keras
(for example, TensorFlow 2.x) and Low-level APIs are supported. A standard
TensorFlow 2.x model can be converted by using TFLite Converter and then
saved in a TFLite FlatBuffer format (named .tflite), which is then executed by
the TFLite interpreter on available devices (GPUs, CPUs) and on native device
APIs. The concrete function in Figure 2 defines a graph that can be converted to a
TensorFlow Lite model or be exported to a SavedModel.
Using TensorFlow Lite
Using TensorFlow Lite involves the following steps:
1. Model selection: A standard TensorFlow 2.x model is selected for solving a
specific task. This can be either a custom-built model or a pretrained model.
2. Model conversion: The selected model is converted with the TensorFlow
Lite converter, generally invoked with a few lines of Python code.
3. Model deployment: The converted model is deployed on the chosen device,
either a phone or an IoT device and then run by using the TensorFlow Lite
interpreter. As discussed, APIs are available for multiple languages.
4. Model optimization: The model can be optionally optimized by using the
TensorFlow Lite optimization framework:
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