A tunable CMOS circuit comprising a CMOS element and a tunable load. The CMOS element is configured to receive an analogue input signal. The tunable load is connected to the CMOS element and configured to set a switch point of the CMOS element. The CMOS element is configured to output an output current that is largest when the analogue input signal is equal to the switch point. The combination of a CMOS element with a tunable load may also provide a hardware implementation of fuzzy logic. A fuzzy logic gate comprises an input node, a CMOS logic gate, a tunable load, and an output node. The input node is configured to receive an analogue input signal. The CMOS logic gate is connected to the input node. The tunable load is connected to the CMOS logic gate such that the tunable load is provided on a current path connected to the output node. The output node is configured to output an analogue output signal.

A method of operation of a semiconductor device that includes the steps of coupling each of a plurality of digital inputs to a corresponding row of non-volatile memory (NVM) cells that stores an individual weight, initiating a read operation based on a digital value of a first bit of the plurality of digital inputs, accumulating along a first bit-line coupling a first array column weighted bit-line current, in which the weighted bit-line current corresponds to a product of the individual weight stored therein and the digital value of the first bit, and converting and scaling, an accumulated weighted bit-line current of the first column, into a scaled charge of the first bit in relation to a significance of the first bit.

A static random-access memory (SRAM) system includes SRAM cells configured to perform exclusive NOR operations between a stored binary weight value and a provided binary input value. In some embodiments, SRAM cells are configured to perform exclusive NOR operations between a stored binary weight value and a provided ternary input value. The SRAM cells are suitable for the efficient implementation of emerging deep neural network technologies such as binary neural networks and XNOR neural networks.

Numerous embodiments for reading a value stored in a selected memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one embodiment, an input comprises a set of input bits that result in a series of input pulses applied to a terminal of the selected memory cell, further resulting in a series of output signals that are summed to determine the value stored in the selected memory cell. In another embodiment, an input comprises a set of input bits, where each input bit results in a single pulse or no pulse being applied to a terminal of the selected memory cell, further resulting in a series of output signals which are then weighted according to the binary bit location of the input bit, and where the weighted signals are then summed to determine the value stored in the selected memory cell.

Encoding of domain logic rules in an analog content addressable memory (aCAM) is disclosed. By encoding domain logic in an aCAM, rapid and flexible search capabilities are enabled, including the capability to search ranges of analog values, fuzzy match capabilities, and optimized parameter search capabilities. This is achieved with low latency by using only a small number of clock cycles at low power. A domain logic ruleset may be represented using various data structures such as decision trees, directed graphs, or the like. These representations can be converted to a table of values, where each table column can be directly mapped to a corresponding row of the aCAM.

A device includes a non-volatile analog resistive memory cell. The non-volatile analog resistive memory device includes a resistive memory device and a select transistor. The resistive memory device includes a first terminal and a second terminal. The resistive memory device has a tunable conductance. The select transistor is a ferroelectric field-effect transistor (FeFET) device which includes a gate terminal, a source terminal, and a drain terminal. The gate terminal of the FeFET device is connected to a word line. The source terminal of the FeFET device is connected to a source line. The drain terminal of the FeFET device is connected to the first terminal of the resistive memory device. The second terminal of the resistive memory device is connected to a bit line.

An analog error correction circuit is disclosed that implements an analog error correction code. The analog circuit includes a crossbar array of memristors or other nonvolatile tunable resistive memory devices. The crossbar array includes a first crossbar array portion programmed with values of a target computation matrix and a second crossbar array portion programmed with values of an encoder matrix for correcting computation errors in the matrix multiplication of an input vector with the computation matrix. The first and second crossbar array portions share the same row lines and are connected to a third crossbar array portion that is programmed with values of a decoder matrix, thereby enabling single-cycle error detection. A computation error is detected based on output of the decoder matrix circuitry and a location of the error is determined via an inverse matrix multiplication operation whereby the decoder matrix output is fed back to the decoder matrix.

A device includes a non-volatile analog resistive memory cell. The non-volatile analog resistive memory device includes a resistive memory device and a select transistor. The resistive memory device includes a first terminal and a second terminal. The resistive memory device has a tunable conductance. The select transistor is a ferroelectric field-effect transistor (FeFET) device which includes a gate terminal, a source terminal, and a drain terminal. The gate terminal of the FeFET device is connected to a word line. The source terminal of the FeFET device is connected to a source line. The drain terminal of the FeFET device is connected to the first terminal of the resistive memory device. The second terminal of the resistive memory device is connected to a bit line.

An analog memory device includes a first node and a second node. The first node includes a first floating gate, a second floating gate, and a capacitor. The first node first floating gate is connected to the first node second floating gate via the capacitor. The second node includes a first floating gate, a second floating gate, and a capacitor. The second node first floating gate is connected to the second node second floating gate via the capacitor. The second node is connected to the first node, and an analog state of the first node and an analog state of the second node continuously and synchronously decay with respect to time.

A static random-access memory (SRAM) system includes SRAM cells configured to perform exclusive NOR operations between a stored binary weight value and a provided binary input value. In some embodiments, SRAM cells are configured to perform exclusive NOR operations between a stored binary weight value and a provided ternary input value. The SRAM cells are suitable for the efficient implementation of emerging deep neural network technologies such as binary neural networks and XNOR neural networks.