A semiconductor device that has low power consumption and is capable of performing arithmetic operation is provided. The semiconductor device includes first to third circuits and first and second cells. The first cell includes a first transistor, and the second cell includes a second transistor. The first and second transistors operate in a subthreshold region. The first cell is electrically connected to the first circuit, the first cell is electrically connected to the second and third circuits, and the second cell is electrically connected to the second and third circuits. The first cell sets current flowing from the first circuit to the first transistor to a first current, and the second cell sets current flowing from the second circuit to the second transistor to a second current. At this time, a potential corresponding to the second current is input to the first cell. Then, a sensor included in the third circuit supplies a third current to change a potential of the second wiring, whereby the first cell outputs a fourth current corresponding to the first current and the amount of change in the potential.

Technology for reconfigurable input precision in-memory computing is disclosed herein. Reconfigurable input precision allows the bit resolution of input data to be changed to meet the requirements of in-memory computing operations. Voltage sources (that may include DACs) provide voltages that represent input data to memory cell nodes. The resolution of the voltage sources may be reconfigured to change the precision of the input data. In one parallel mode, the number of DACs in a DAC node is used to configure the resolution. In one serial mode, the number of cycles over which a DAC provides voltages is used to configure the resolution. The memory system may include relatively low resolution voltage sources, which avoids the need to have complex high resolution voltage sources (e.g., high resolution DACs). Lower resolution voltage sources can take up less area and/or use less power than higher resolution voltage sources.

A near memory system is provided for the calculation of a layer in a machine learning application. The near memory system includes an array of memory cells for storing an array of filter weights. A multiply-and-accumulate circuit couples to columns of the array to form the calculation of the layer.

A semiconductor device capable of efficiently recognizing images utilizing a neural network is provided. The semiconductor device includes a shift register group, a D/A converter, and a product-sum operation circuit. The product-sum operation circuit includes an analog memory and stores a parameter of a filter. The shift register group captures image data and outputs part of the image data to the D/A converter while shifting the image data. The D/A converter converts the part of the input image data into analog data and outputs the analog data to the product-sum operation circuit.

A reservoir includes a common input layer, first and second output layers that outputs a first and a second readout values based on an input, a first partial reservoir including the input layer and the first output layer, and a second partial reservoir having a size between the input layer and the second output layer larger than the size of the first partial reservoir, and the training processing including: first calculating a third output weight that reduces a difference between a first product sum value of a third readout value and a first output weight; and second calculating a fourth output weight that reduces a difference between a second product sum value of a fourth readout value and a second output weight and differential teaching data that is a difference between a third product sum value of the third readout value and the third output weight and the teaching data.

A device includes a multiplication unit and a configurable summing unit. The multiplication unit is configured to receive data and weights for an Nth layer, where N is a positive integer. The multiplication unit is configured to multiply the data by the weights to provide multiplication results. The configurable summing unit is configured by Nth layer values to receive an Nth layer number of inputs and perform an Nth layer number of additions, and to sum the multiplication results and provide a configurable summing unit output.

A computing method for performing a matrix multiplying-and-accumulating computation by a flash memory array which includes word lines, bit lines and flash memory cells. The computing method includes the following steps: respectively storing a weight value in each of the flash memory cells, receiving a plurality of input voltages via the word lines, performing an computation on one of the input voltages and the weight value by each of the flash memory cells to obtain an output current, outputting the output currents of the flash memory cells via the bit lines, and accumulating the output currents of the flash memory cells connected to the same bit line of the bit lines to obtain a total output current. Each of the flash memory cells is an analog device, and each of the input voltages, each of the output currents and each of the weight values are analog values.

A PIM device includes a memory/arithmetic region including a plurality of memory banks and a plurality of MAC operators, the plurality of MAC operators including a first MAC operator, a peripheral region including a data input/output circuit, and a global data input/output (GIO) line capable of providing a data transmission path between the peripheral region and the memory/arithmetic region. The first MAC operator is configured to perform an EWM operation by performing a multiplication operation on first input data and second input data that are transmitted from first and second memory banks of the plurality of memory banks, respectively, to generate multiplication result data and transmitting the multiplication result data to a third memory bank. While the EWM operation is being performed, data transmission through the GIO line between the peripheral region and the memory/arithmetic region is blocked.

An in-memory computing system for computing vector-matrix multiplications includes an array of resistive memory devices arranged in columns and rows, such that resistive memory devices in each row of the array are interconnected by a respective word line and resistive memory devices in each column of the array are interconnected by a respective bitline. The in-memory computing system also includes an interface circuit electrically coupled to each bitline of the array of resistive memory devices and computes the vector-matrix multiplication between an input vector applied to a given set of word lines and data values stored in the array. For each bitline, the interface circuit receives an output in response to the input being applied to the given wordline, compares the output to a threshold, and increments a count maintained for each bitline when the output exceeds the threshold. The count for a given bitline represents a dot-product.

A system is provided. The system includes a multiply-and-accumulate circuit and a local generator. The multiply-and-accumulate circuit is coupled to a memory array and generates a multiply-and-accumulate signal indicating a computational output of the memory array. The local generator is coupled to the memory array and generates at least one reference signal at a node in response to one of a plurality of global signals that are generated according to a number of the computational output. The local generator is further configured to generate an output signal according to the signal and a summation of the at least one reference signal at the node.