Abstract: Training of a machine learning model used to infer estimated delays of circuit routes during placement and routing of a circuit design. Training can include selecting sample pairs of source pins and destination pins of an integrated circuit (IC) device, and determining respective delays of shortest paths that connect the source pins to the destination pins of the sample pairs based on a resistance-capacitance model of wires that form the shortest paths on the IC device. Respective sets of features are determined for the shortest paths, and the model is trained using the respective sets of features and the respective delays as labels. The machine learning model can be provided to an electronic design automation tool for estimating delays.

Abstract: In one embodiment, a handheld device for applying therapy includes a housing, a handle coupled to the housing, a motor supported in the housing, a movable head coupled to the motor, and a contact pad of high-friction, grippy, non-slip material supported on the movable head. The motor is operable to impart to the movable head and the contact pad one or more of translational, rotational, or revolutionary motions that are generally in a plane parallel to the skin of a patient during operation.

Abstract: In one embodiment, a handheld device for applying therapy includes a housing, a handle coupled to the housing, a motor supported in the housing, a movable head coupled to the motor, and a contact pad of high-friction, grippy, non-slip material supported on the movable head. The motor is operable to impart to the movable head and the contact pad one or more of translational, rotational, or revolutionary motions that are generally in a plane parallel to the skin of a patient during operation.