A neuromorphic hardware apparatus based on a resistive memory array includes a resistive memory array in which a plurality of synaptic resistor elements are arranged. Each synaptic resistor element is changed in its resistance value depending on a voltage pulse applied thereto and stores the resistance value for a predetermined time. The apparatus also includes a neuron circuit configured to receive an output signal from the resistive memory array and to output a voltage signal to another resistive memory array. The neuron circuit includes a temperature compensation unit, which compensates for an output voltage of the resistive memory array on the basis of an operating temperature of the resistive memory array. Even when a resistive memory array outputs an abnormal output depending on an operating temperature, by compensating a neuron circuit for an input value, it is possible to prevent an operation error from occurring.

A neuromorphic hardware apparatus based on a resistive memory array includes a resistive memory array in which a plurality of synaptic resistor elements are arranged. Each synaptic resistor element is changed in its resistance value depending on a voltage pulse applied thereto and stores the resistance value for a predetermined time. The apparatus also includes a neuron circuit configured to receive an output signal from the resistive memory array and to output a voltage signal to another resistive memory array. The neuron circuit includes a temperature compensation unit, which compensates for an output voltage of the resistive memory array on the basis of an operating temperature of the resistive memory array. Even when a resistive memory array outputs an abnormal output depending on an operating temperature, by compensating a neuron circuit for an input value, it is possible to prevent an operation error from occurring.

Provided is an artificial neural network circuit including unit weight memory cells including weight memory devices configured to store weight data and weight pass transistors, unit threshold memory cells including a threshold memory device programmed to store a threshold and a threshold pass transistor, a weight-threshold column in which the plurality of unit weight memory cells and the plurality of unit threshold memory cells are connected, and a sense amplifier configured to receive an output signal of the weight-threshold column as an input and receive a reference voltage as another input.

Provided is an artificial neural network circuit including unit weight memory cells including weight memory devices configured to store weight data and weight pass transistors, unit threshold memory cells including a threshold memory device programmed to store a threshold and a threshold pass transistor, a weight-threshold column in which the plurality of unit weight memory cells and the plurality of unit threshold memory cells are connected, and a sense amplifier configured to receive an output signal of the weight-threshold column as an input and receive a reference voltage as another input.

Digital compute-in-memory (DCIM) bit cell circuit layouts and DCIM array circuits for multiple operations per column are disclosed. A DCIM bit cell array circuit including DCIM bit cell circuits comprising exemplary DCIM bit cell circuit layouts disposed in columns is configured to evaluate the results of multiple multiply operations per clock cycle. The DCIM bit cell circuits in the DCIM bit cell circuit layouts each couples to one of a plurality of column output lines in a column. In this regard, in each cycle of a system clock, each of the plurality of column output lines receives a result of a multiply operation of a DCIM bit cell circuit coupled to the column output line. The DCIM bit cell array circuit includes digital sense amplifiers coupled to each of the plurality of column output lines to reliably evaluate a result of a plurality of multiply operations per cycle.

Various embodiments provide methods for configuring a phase-change random-access memory (PCRAM) structures, such as PCRAM operating in a single-level-cell (SLC) mode or a multi-level-cell (MLC) mode. Various embodiments may support a PCRAM structure being operating in a SLC mode for lower power and a MLC mode for lower variability. Various embodiments may support a PCRAM structure being operating in a SLC mode or a MLC mode based at least in part on an error tolerance for a neural network layer.

Systems are provided for implementing a hardware accelerator. The hardware accelerator emulate a stochastic neural network, and includes a first memristor crossbar array, and a second memristor crossbar array. The first memristor crossbar array can be programmed to calculate node values of the neural network. The nodes values can be calculated in accordance with rules to reduce an energy function associated with the neural network. The second memristor crossbar array is coupled to the first memristor crossbar array and programmed to introduce noise signals into the neural network. The noise signals can be introduced such that the energy function associated with the neural network converges towards a global minimum and modifies the calculated node values.

Provided are processing and an electronic device including the same. The processing apparatus includes a bit cell line comprising bit cells connected in series, a mirror circuit unit configured to generate a mirror current by replicating a current flowing through the bit cell line at a ratio, a charge charging unit configured to charge a voltage corresponding to the mirror current as the mirror current replicated by the mirror circuit unit is applied, and a voltage measuring unit configured to output a value corresponding to a multiply-accumulate (MAC) operation of weights and inputs applied to the bit cell line, based on the voltage charged by the charge charging unit.

Artificial neuromorphic circuit includes synapse and post-neuron circuits. Synapse circuit includes phase change element and receives first and second pulse signals. Post-neuron circuit includes input, output and integration terminals. Integration terminal is charged to membrane potential according to first pulse signal. Post-neuron circuit further includes first and second control circuits, and first and second delay circuits. First control circuit generates firing signal at output terminal based on membrane potential. Second control circuit generates first control signal based on firing signal. First delay circuit delays firing signal to generate second control signal. Second delay circuit delays second control signal to generate third control signal. First and third control signals control voltage level of integration terminal, maintain integration terminal at fixed voltage during period, and second control signal cooperates with second pulse signal to control state of phase change element to determine weight of artificial neuromorphic circuit.

Artificial neuromorphic circuit includes synapse and post-neuron circuits. Synapse circuit includes phase change element and receives first and second pulse signals. Post-neuron circuit includes input, output and integration terminals. Integration terminal is charged to membrane potential according to first pulse signal. Post-neuron circuit further includes first and second control circuits, and first and second delay circuits. First control circuit generates firing signal at output terminal based on membrane potential. Second control circuit generates first control signal based on firing signal. First delay circuit delays firing signal to generate second control signal. Second delay circuit delays second control signal to generate third control signal. First and third control signals control voltage level of integration terminal, maintain integration terminal at fixed voltage during period, and second control signal cooperates with second pulse signal to control state of phase change element to determine weight of artificial neuromorphic circuit.