Posted by the TensorFlow team
TensorFlow 2.20 has been released! For ongoing updates related to the multi-backend Keras, please note that all news and releases, starting with Keras 3.0, are now published directly on keras.io. You can find a com…
Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi – Harvard University
TinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge dev…
Posted by Alan Kelly, Software Engineer
We are excited to announce that XNNPack’s Fully Connected and Convolution 2D operators now support dynamic range quantization. XNNPack is TensorFlow Lite’s CPU backend and CPUs deliver the widest reach fo…
Posted by Marat Dukhan and Frank Barchard, Software Engineers
CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority, and we are exci…
Posted by Wei Wei, Developer Advocate
In our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Li…
Posted by Alan Kelly, Software Engineer
One of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite’s memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on eve…
Posted by Angelica Willis and Akib Uddin, Health AI Team, Google Research
How researchers at Google are working to expand global access to maternal healthcare with the help of AI
TensorFlow Lite* is an open-source framework to run machine lear…