Abstract: A prediction engine predicts the performance of a neural network model executed on a hardware platform. The neural network model is compiled for the hardware platform. The neural network model includes multiple layers and each layer is defined by a set of operations and corresponding configuration settings of the operations. For each layer, the prediction engine performs feature embedding on the set of operations and the corresponding configuration settings to generate a feature embedded sequence of categorical feature vectors and numerical feature vectors. Positional encoding and a series of attention functions are applied on the feature embedded sequence to generate an encoded sequence. The prediction engine reduces the dimensions of the encoded sequence to output a performance metric of executing the neural network model on the hardware platform.

Abstract: According to a network space search method, an expanded search space is partitioned into multiple network spaces. Each network space includes a plurality of network architectures and is characterized by a first range of network depths and a second range of network widths. The performance of the network spaces is evaluated by sampling respective network architectures with respect to a multi-objective loss function. The evaluated performance is indicated as a probability associated with each network space. The method then identifies a subset of the network spaces that has the highest probabilities, and selects a target network space from the subset based on model complexity.

Abstract: A compensator compensates for the distortions of a power amplifier circuit. A power amplifier neural network (PAN) is trained to model the power amplifier circuit using pre-determined input and output signal pairs that characterize the power amplifier circuit. Then a compensator is trained to pre-distort a signal received by the PAN. The compensator uses a neural network trained to optimize a loss between a compensator input and a PAN output, and the loss is calculated according to a multi-objective loss function that includes one or more time-domain loss function and one or more frequency-domain loss functions. The trained compensator performs signal compensation to thereby output a pre-distorted signal to the power amplifier circuit.

Abstract: Disclosed relates to a method and system of determining and recording eye motions. It is a distance measuring photoelectric proximity sensor fixed to the head, which senses the change in distance between the sensor and the eye when the eyelid is opening and closing to detect the eye open and close motion; with its small size and power saving features, it can be used as a head-mounted device for real-time management and long-term recording of eye usage, and is applied to human-machine interfaces to transmit signals generated by user's eye motions to control external components.

Abstract: An inductive device including an inductor coil located over a substrate, at least one electrically insulating layer interposing the inductor coil and the substrate, and a plurality of current interrupters each extending into the substrate, wherein a first aggregate outer boundary of the plurality of current interrupters substantially encompasses a second aggregate outer boundary of the inductor coil.