Abstract: A computation operator in memory and an operation method thereof are provided. The computation operator in memory includes a word line calculator, a decision-maker and a sense amplifier. The word line calculator calculates a number of enabled word lines of a memory. The decision-maker generates a plurality of reference signals according to at least one of the number of enabled word lines and a used size of the memory, the reference signals are configured to set a distribution range. The sense amplifier receives a readout signal of the memory, and obtains a computation result by converting the readout signal according to the reference signals.

Abstract: A transpose memory unit for a plurality of multi-bit convolutional neural network based computing-in-memory applications includes a memory cell and a transpose cell. The memory cell stores a weight. The transpose cell is connected to the memory cell and receives the weight from the memory cell. The transpose cell includes an input bit line, at least one first input word line, a first output bit line, at least one second input word line and a second output bit line. One of the at least one first input word line and the at least one second input word line transmits at least one multi-bit input value, and the transpose cell is controlled by the second word line to generate a multiply-accumulate output value on one of the first output bit line and the second output bit line according to the at least one multi-bit input value multiplied by the weight.

Abstract: An apparatus and a method for neural network computation are provided. The apparatus for neural network computation includes a first neuron circuit and a second neuron circuit. The first neuron circuit is configured to execute a neural network computation of at least one computing layer with a fixed feature pattern in a neural network algorithm. The second neuron circuit is configured to execute the neural network computation of at least one computing layer with an unfixed feature pattern in the neural network algorithm. The performance of the first neuron circuit is greater than that of the second neuron circuit.

Abstract: A computer-implemented method for a monitoring device being executed by a processor of the monitoring device comprises acquiring at least two kinds of physiological signals via a sensor of the monitoring device, obtaining a signal quality index of each of the at least two kinds of physiological signals by the processor of the monitoring device, providing a homologous physiological parameter value corresponding to each of the at least two kinds of physiological signals by the processor of the monitoring device, and fusing the homologous physiological parameter values based on the signal quality index of each of the at least two kinds of physiological signals and providing a fused value of the homologous physiological parameter values by the processor of the monitoring device. The disclosed physiological parameter processing method avoids disadvantages caused by relying on a single physiological signal.

Abstract: A neural circuit is provided. The neural circuit includes a neural array. The neural array includes a plurality of semiconductor components. Each of the semiconductor components stores a weighting value to generate a corresponding output current or a corresponding equivalent resistance. The neural array receives a plurality of input signals to control the semiconductor components in the neural array and respectively generates the output currents or changes the equivalent resistances. Since the semiconductor components are coupled to each other, output of the neural array may generate a summation current or a summation equivalent resistance related to the input signals and a weighting condition, so that a computing result exhibits high performance.

Abstract: A computer-implemented method for a monitoring device being executed by a processor of the monitoring device comprises acquiring at least two kinds of physiological signals via a sensor of the monitoring device, obtaining a signal quality index of each of the at least two kinds of physiological signals by the processor of the monitoring device, providing a homologous physiological parameter value corresponding to each of the at least two kinds of physiological signals by the processor of the monitoring device, and fusing the homologous physiological parameter values based on the signal quality index of each of the at least two kinds of physiological signals and providing a fused value of the homologous physiological parameter values by the processor of the monitoring device. The disclosed physiological parameter processing method avoids disadvantages caused by relying on a single physiological signal.