New ops and controls in Neural Networks API - Activation functions control the output of nodes within a neural network. At Google AI, we discovered a swish activation function allowing for faster training time and higher accuracy across a wide variety of tasks. In Android 11, we’re adding a computationally efficient version of this function, the hard-swish op. This is key to accelerating next-generation on-device vision models such as MobileNetV3 which forms the base model for many transfer learning use cases. Another major addition is the Control ops enabling more advanced machine learning models that support branching and loops. Finally, we’ve also added new execution controls to help you minimize latency for common use cases: Asynchronous Command Queue APIs reduce the overhead when running small chained models. See the NDK sample code for examples using these new APIs.