Abstract: Apparatuses, systems, and techniques for designing a data path circuit such as a parallel prefix circuit with reinforcement learning are described. A method can include receiving a first design state of a data path circuit, inputting the first design state of the data path circuit into a machine learning model, and performing reinforcement learning using the machine learning model to output a final design state of the data path circuit, wherein the final design state of the data path circuit has decreased area, power consumption and/or delay as compared to conventionally designed data path circuits.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.

Abstract: A variable transmission output coupler and deviationless tuner forms a part of a tunable laser cavity. The device uses frustrated total internal reflection to provide variable output coupling and laser tunability simultaneously. Two Pellin-Broca prisms are pressed against each other by a variable spring pressure mechanism. An adjustable screw regulates the variable force, controlling the thickness of an air film between two prism faces. The Pellin-Broca prisms enable tuning the laser without angular deviation of the output beam. All beams enter and exit at Brewster's Angle, minimizing losses. No coatings are used on the optical elements, reducing the possibility of damage at high operating power of the laser. The coupler may be used in pulsed as well as continuous wave lasers.