Abstract: A computing in memory (CIM) cell includes a memory cell circuit, a first semiconductor element, a second semiconductor element, a third semiconductor element, and a fourth semiconductor element. A first terminal of the first semiconductor element receives a bias voltage. A control terminal of the first semiconductor element is coupled to a computing word-line. A control terminal of the second semiconductor element is coupled to a first data node in the memory cell circuit. A second terminal of the third semiconductor element is adapted to receive a reference voltage. A control terminal of the third semiconductor element receives an inverted signal of the computing word-line. A first terminal of the fourth semiconductor element is coupled to a first computing bit-line. A second terminal of the fourth semiconductor element is coupled to a second computing bit-line.

Abstract: A computing in memory (CIM) cell includes a memory cell circuit, a first semiconductor element, a second semiconductor element, and a third semiconductor element. A first terminal of the first semiconductor element is coupled to a first computing bit-line. A control terminal of the first semiconductor element is coupled to a computing word-line. A control terminal of the second semiconductor element is coupled to the memory cell circuit. A first terminal of the second semiconductor element is coupled to a second terminal of the first semiconductor element. A first terminal of the third semiconductor element is coupled to a second terminal of the second semiconductor element. A second terminal of the third semiconductor element is coupled to a second computing bit-line. A control terminal of the third semiconductor element receives a bias voltage.

Abstract: A configurable computing unit within memory including a first input transistor, a first weight transistor, a first resistor, a second input transistor, a second weight transistor, and a second resistor is provided. The first input transistor, the first weight transistor, and the first resistor are coupled in series between a first readout bit line and a common signal line. The first input transistor is coupled to a first input bit line, and the first weight transistor receives a first weight bit. The second input transistor, the second weight transistor, and the second resistor are coupled in series between the first readout bit line and the common signal line. The second input transistor is coupled to a second input bit line, and the second weight transistor receives the second weight bit.

Abstract: A memory device for in-memory computation includes data channels, a memory cell array, a maximum accumulated weight generating array, a minimum accumulated weight generating array, a reference generator and a comparator. The data channels are selectively enabled according to data input. The memory cell array generates an accumulated data weight value according to the quantity of enabled data channels, a first resistance and a second resistance. The maximum accumulated weight generating array generates a maximum accumulated weight value according to the quantity of enabled data channels and the first resistance. The minimum accumulated weight generating array generates a minimum accumulated weight value according to the quantity of enabled data channels and the second resistance. The reference generator generates reference value(s) according to the maximum and minimum accumulated weight values.

Abstract: A memory structure with input-aware maximum multiply-and-accumulate value zone prediction for computing-in-memory applications includes a memory array, an input-aware zone prediction circuit and an analog-to-digital converter. An input-aware maximum partial multiply-and-accumulate value voltage generator is configured to generate a maximum partial multiply-and-accumulate value according to at least one input value. A prediction-aware global reference voltage generator is configured to generate a plurality of global reference voltages, a maximum reference voltage and a selected minimum reference voltage. A maximum partial multiply-and-accumulate value zone detector is configured to generate a zone switch signal by comparing the maximum partial multiply-and-accumulate value and the global reference voltages.

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: A memory device for in-memory computation includes data channels, a memory cell array, a maximum accumulated weight generating array, a minimum accumulated weight generating array, a reference generator and a comparator. The data channels are selectively enabled according to data input. The memory cell array generates an accumulated data weight value according to the quantity of enabled data channels, a first resistance and a second resistance. The maximum accumulated weight generating array generates a maximum accumulated weight value according to the quantity of enabled data channels and the first resistance. The minimum accumulated weight generating array generates a minimum accumulated weight value according to the quantity of enabled data channels and the second resistance. The reference generator generates reference value(s) according to the maximum and minimum accumulated weight values.

Abstract: A memory unit includes at least one memory cell and a computational cell. The at least one memory cell stores a weight. The at least one memory cell is controlled by a first word line and includes a local bit line transmitting the weight. The computational cell is connected to the at least one memory cell and receiving the weight via the local bit line. Each of an input bit line and an input bit line bar transmits a multi-bit input value. The computational cell is controlled by a second word line and an enable signal to generate a multi-bit output value on each of an output bit line and an output bit line bar according to the multi-bit input value multiplied by the weight. The computational cell is controlled by a first switching signal and a second switching signal for charge sharing.

Abstract: A computing in memory (CIM) cell includes a memory cell circuit, a first semiconductor element, a second semiconductor element, and a third semiconductor element. A first terminal of the first semiconductor element is coupled to a first computing bit-line. A control terminal of the first semiconductor element is coupled to a computing word-line. A control terminal of the second semiconductor element is coupled to the memory cell circuit. A first terminal of the second semiconductor element is coupled to a second terminal of the first semiconductor element. A first terminal of the third semiconductor element is coupled to a second terminal of the second semiconductor element. A second terminal of the third semiconductor element is coupled to a second computing bit-line. A control terminal of the third semiconductor element receives a bias voltage.

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 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.