An introduction to TensorFlow Lite Converter, Quantized Optimization, and Interpreter to run Tensorflow Lite models at the Edge

In this article, we will understand the features required to deploy a deep learning model at the Edge, what is TensorFlow Lite, and how the different components of TensorFlow Lite can be used to make an inference at the Edge.

You are trying to deploy your deep learning model in an area where they don’t have a good network connection but still need the deep learning model to give an excellent performance.

TensorFlow Lite can be used in such a scenario

Features of a Deep Learning model to make inference at the Edge

1. Light-weight: Edge devices have limited resources in terms of storage and computation capacity. Deep learning models are resource-intensive, so the models we deploy on edge devices should be light-weight with smaller binary sizes.
2. **Low Latency: **Deep Learning models at the Edge should make faster inferences irrespective of network connectivity. As the inferences are made on the Edge device, a round trip from the device to the server will be eliminated, making inferences faster.
3. **Secure: **The Model is deployed on the Edge device, the inferences are made on the device, no data leaves the device or is shared across the network, so there is no concern for data privacy.
4. **Optimal power consumption: **Network needs a lot of power, and Edge devices may not be connected to the network, and hence, the power consumption need is low.
5. Pre-trained: Models can be trained on-prem or cloud for different deep learning tasks like image classification, object detection, speech recognition, etc. and can be easily deployed to make inferences at the Edge.

Tensorflow Lite offers all the features required for making inferences at the Edge.

But what is TensorFlow Lite?

TensorFlow Lite is an open-source, product ready, cross-platform deep learning framework that converts a pre-trained model in TensorFlow to a special format that can be optimized for speed or storage.

The special format model can be deployed on edge devices like mobiles using Android or iOS or Linux based embedded devices like Raspberry Pi or Microcontrollers to make the inference at the Edge.

How does Tensorflow Lite(TF Lite) work?

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