Abstract: In various embodiments, a congestion control modelling application automatically controls congestion in data transmission networks. The congestion control modelling application executes a trained neural network in conjunction with a simulated data transmission network to generate a training dataset. The trained neural network has been trained to control congestion in the simulated data transmission network. The congestion control modelling application generates a first trained decision tree model based on an initial loss for an initial model relative to the training dataset. The congestion control modelling application generates a final tree-based model based on the first trained decision tree model and at least a second trained decision tree model. The congestion control modelling application executes the final tree-based model in conjunction with a data transmission network to control congestion within the data transmission network.

Abstract: A method for approximating a mathematical function defined over a range includes initially dividing at least part of the range into a set of segments. For at least a subset of the segments, the mathematical function is approximated within each segment by a respective approximation polynomial. A series of one or more segment-merging iterations is performed, a given iteration including: selecting adjacent segments as candidates for merging; approximating the mathematical function by a candidate approximation polynomial, over at least a merged segment formed by merging the adjacent segments; and, if approximation of the mathematical function meets a specified condition, updating the set of segments by (i) replacing the adjacent segments with the merged segment and (ii) replacing the approximation polynomials of the adjacent segments with the candidate approximation polynomial.

Abstract: Apparatuses, systems, and techniques to improve processing efficiency. In at least one embodiment, a processing unit includes circuitry that reads a vector from memory and multiplies the vector with a scalar value extracted from a scalar field of a vector register. The scalar field may be specified by an immediate field value that is also used to identify an offset used to define a pointer that points to a location in the memory from which to read the vector.

Abstract: Apparatuses, systems, and techniques to improve processing efficiency are provided. In at least one embodiment, a processing unit is described as including circuitry that receives an input vector and applies an activation function to the input vector by performing a hardware approximation of the activation function in a vector manner. The circuitry also generates an output vector based on the activation function.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.