Abstract: A super resolution device and method are provided. The device comprises an input interface and a sharing layer calculator group. An image and a scaling signal are received by the input interface, wherein the scaling signal is configured to indicate to perform a double scaling operation or a quadruple scaling operation to the image. When the scaling signal indicates to perform the quadruple scaling operation, the sharing layer calculator group performs a plurality of convolution operations based on a first number of input channels and a first number of output channels. When the scaling signal indicates to perform the double scaling operation, the sharing layer calculator group performs the convolution operations based on a second number of input channels and a second number of output channels.

Abstract: A memory control system includes a front-end circuitry, a back-end circuitry, and a traffic scheduling circuitry. The front-end circuitry is configured to receive a plurality of access requests from a plurality of devices, and adjust an order of the plurality of devices to access a memory according to a plurality of control signals. The traffic scheduling circuitry is configured to generate a plurality of traffic data based on the plurality of access requests and analyze the plurality of traffic data based on a neural network model and a predetermined rule, in order to determine the plurality of control signals. The back-end circuitry is configured to adjust a task schedule of the memory according to the plurality of control signals.

Abstract: A memory control system includes a front-end circuitry, a back-end circuitry, and a traffic scheduling circuitry. The front-end circuitry is configured to receive a plurality of access requests from a plurality of devices, and adjust an order of the plurality of devices to access a memory according to a plurality of control signals. The traffic scheduling circuitry is configured to generate a plurality of traffic data based on the plurality of access requests and analyze the plurality of traffic data based on a neural network model and a predetermined rule, in order to determine the plurality of control signals. The back-end circuitry is configured to adjust a task schedule of the memory according to the plurality of control signals.

Abstract: A super resolution device and method are provided. The device comprises an input interface and a sharing layer calculator group. An image and a scaling signal are received by the input interface, wherein the scaling signal is configured to indicate to perform a double scaling operation or a quadruple scaling operation to the image. When the scaling signal indicates to perform the quadruple scaling operation, the sharing layer calculator group performs a plurality of convolution operations based on a first number of input channels and a first number of output channels. When the scaling signal indicates to perform the double scaling operation, the sharing layer calculator group performs the convolution operations based on a second number of input channels and a second number of output channels.