Optimized BNN inference for edge devices
Monday, February 17, 2020
We believe BNNs are the future of efficient inference, which is why we’ve developed tools to make it easier to train and research these models. Our open-source library Larq enables developers to build and train BNNs and integrates seamlessly with TensorFlow Keras. Larq Zoo provides implementations of major BNNs from the literature together with pretrained weights for state-of-the-art models.
But the ultimate goal of BNNs is to solve real-world problems on the edge. So once you’ve built and trained a BNN with Larq, how do you get it ready for efficient inference? Today, we’re introducing Larq Compute Engine to tackle that problem.