How TensorFlow Lite Optimizes Neural Networks for Mobile Machine Learning


The steady rise of mobile Internet traffic has provoked a parallel increase in demand for on-device intelligence capabilities. However, the inherent scarcity of resources at the Edge means that satisfying this demand will require creative solutions to old problems. How do you run computationally expensive operations on a device that has limited processing capability without it turning into magma in your hand? The addition of TensorFlow Lite to the TensorFlow ecosystem provides us with the next step forward in machine learning capabilities, allowing us to harness the power of TensorFlow models on mobile and embedded devices while maintaining low latency, efficient runtimes, and accurate inference. TensorFlow Lite provides the framework for a trained TensorFlow model to be compressed and deployed to a mobile or embedded application.

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