A charge sharing Compute In Memory (CIM) may comprise an XNOR bit cell with an internal capacitor between the XNOR output node and a system voltage. Alternatively, a charge sharing CIM may comprise an XNOR bit cell with an internal capacitor between the XNOR output node and a read bit line. Alternatively, a charge sharing CIM may comprise an XNOR bit cell with an internal cap between XNOR and read bit line with a separate write bit line and write bit line bar.

Disclosed are systems and methods for performing efficient vector-matrix multiplication using a sparsely-connected conductance matrix and analog mixed signal (AMS) techniques. Metal electrodes are sparsely connected using coaxial nanowires. Each electrode can be used as an input/output node or neuron in a neural network layer. Neural network synapses are created by random connections provided by coaxial nanowires. A subset of the metal electrodes can be used to receive a vector of input voltages and the complementary subset of the metal electrodes can be used to read output currents. The output currents are the result of vector-matrix multiplication of the vector of input voltages with the sparsely-connected matrix of conductances.

A device performs a matrix-vector multiplication of a matrix with a vector. The device includes a crossbar array having row lines, column lines and junctions arranged between the row lines and the column lines. Each junction includes a programmable resistive element and an access element for accessing the programmable resistive element. The device further includes a signal generator configured to apply programming signals to the resistive elements to program conductance values for the matrix-vector multiplication. The device further includes a readout circuit and control circuitry configured to control the signal generator and the readout circuit. The readout circuit is configured to apply read voltages having a positive voltage sign and negative read voltages having a negative voltage sign to the row lines of the crossbar array. The readout circuit is further configured to read out column currents of the plurality of column lines of the crossbar array.

A charge sharing Compute In Memory (CIM) may comprise an XNOR bit cell with an internal capacitor between the XNOR output node and a system voltage. Alternatively, a charge sharing CIM may comprise an XNOR bit cell with an internal capacitor between the XNOR output node and a read bit line. Alternatively, a charge sharing CIM may comprise an XNOR bit cell with an internal cap between XNOR and read bit line with a separate write bit line and write bit line bar.

A fault-tolerant analog computing device includes a crossbar array having a number l rows and a number n columns intersecting the l rows to form ln memory locations. The l rows of the crossbar array receive an input signal as a vector of length l. The n columns output an output signal as a vector of length n that is a dot product of the input signal and the matrix values defined in the ln memory locations. Each memory location is programmed with a matrix value. A first set of k columns of the n columns is programmed with continuous analog target matrix values with which the input signal is to be multiplied, where k<n. A second set of m columns of the n columns is programmed with continuous analog matrix values for detecting an error in the output signal that exceeds a threshold error value, where m<n.

A semiconductor memory device includes a memory cell array including first memory cells and second memory cell, and a peripheral circuit. When a first command, a first address, and first input data are received, the peripheral circuit reads first data from the first memory cells based on the first address in response to the first command, performs a first operation by using the first data and the first input data, and reads second data from the second memory cells by using a result of the first operation.

Analog multipliers can perform signal processing with approximate precision asynchronously (clock free) and with low power consumptions, which can be advantageous including in emerging mobile and portable artificial intelligence (AI) and machine learning (ML) applications near or at the edge and or near sensors. Based on low cost, mainstream, and purely digital Complementary-Metal-Oxide-Semiconductor (CMOS) manufacturing process, the present invention discloses embodiments of current-mode analog multipliers that can be utilized in multiply-accumulate (MAC) signal processing in end-application that require low cost, low power consumption, (clock free) and asynchronous operations.

A multiport memory in which one of the ports is analog rather than digital is described. In one embodiment, the analog port functions as a read-only port and the digital port functions as a write only port. This allows the data in the core memory to be applied to an analog signal, while retaining a digital port having access to the core memory for rapid storage of data. One potential use of such a multiport memory is as a bridge between a digital computer and an analog computer; for example, this allows a digitally programmed two-port memory to derive a sum-of-products signal from a plurality of analog input signals, and a plurality of such multiport memories to be used in an analog neural network such as a programmable neural net implementing analog artificial intelligence (AI).

A weight cell and device are herein disclosed. The weight cell includes a first field effect transistor (FET) and a first resistive memory element connected to a drain of the first FET, a second FET and a second resistive memory element connected to a drain of the second FET, the drain of the first FET being connected to a gate of the second FET and the drain of the second FET is connected to a gate of the first FET, a third FET and a third resistive memory element connected to a drain of the third FET, and a fourth FET and a fourth resistive memory element connected to a drain of the fourth FET, the drain of the third FET is connected to a gate of the fourth FET and the drain of the fourth FET being connected to a gate of the third FET.

A device performs a matrix-vector multiplication of a matrix with a vector. The device includes a crossbar array having row lines, column lines and junctions arranged between the row lines and the column lines. Each junction includes a programmable resistive element and an access element for accessing the programmable resistive element. The device further includes a signal generator configured to apply programming signals to the resistive elements to program conductance values for the matrix-vector multiplication. The device further includes a readout circuit and control circuitry configured to control the signal generator and the readout circuit. The readout circuit is configured to apply read voltages having a positive voltage sign and negative read voltages having a negative voltage sign to the row lines of the crossbar array. The readout circuit is further configured to read out column currents of the plurality of column lines of the crossbar array.