Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a surrogate neural network configured to determine a predicted performance measure of a hardware accelerator having a target hardware configuration on a target application. The trained instance of the surrogate neural network can be used. in addition to or in place of hardware simulation, during a search process for determining hardware configurations for application-specific hardware accelerators. i.e., hardware accelerators on which one or more neural networks can be deployed to perform one or more target machine learning tasks.

Abstract: A method includes determining a first state associated with a particular task, and determining, by a task policy model, a latent space representation of the first state. The task policy model may have been trained to define, for each respective state of a plurality of possible states associated with the particular task, a corresponding latent space representation of the respective state. The method also includes determining, by a primitive policy model and based on the first state and the latent space representation of the first state, an action to take as part of the particular task. The primitive policy model may have been trained to define a space of primitive policies for the plurality of possible states associated with the particular task and a plurality of possible latent space representations. The method further includes executing the action to reach a second state associated with the particular task.