A method of performing a predetermined operation for a circuit that includes a resistor group, one end of the resistor group being configured for connection to a power supply unit, the other end of the resistor group being configured for connection to a sampling capacitor, and a parasitic capacitance existing at each node between resistors of the resistor group. The method includes in a forward process, determining a time when a sampling capacitor voltage applied to the sampling capacitor reaches a first reference voltage as a switching time; at the switching time, connecting the sampling capacitor to a ground or predetermined voltage and floating the power supply unit; in a backward process, after the switching time, determining a time when a power supply unit voltage applied to the power supply unit reaches a second reference voltage as an end time; and performing the predetermined operation based on the end time.

An electronic circuit and a method of making the same includes a multiplier circuit configured to perform a multiplication of a first input signal with a second input signal. The first input signal is a binary input signal that includes a sequence of input bits. The electronic circuit further includes an oscillator circuit configured to receive a result signal of the multiplication from the multiplier and to provide output pulses having an output frequency which is dependent on the result signal of the multiplication and a digital counter circuit configured to count the output pulses. The digital counter circuit is configured to provide a plurality of counter bits and to select one of the plurality of counter bits for incrementation in dependence on a significance of the corresponding input bit of the sequence of input bits.

A neural network apparatus includes: a plurality of memory cells each comprising a variable resistance element and a first transistor; a plurality of bit lines extending in a first direction; and a plurality of word lines extending in a second direction, crossing the bit lines and respectively connected to the first transistor of the plurality of memory cells; a plurality of sub-column circuits each comprising memory cells of the memory cells connected in parallel along the first direction; and a column circuit comprising two or more of the sub-column circuits connected in series along the second direction, wherein, when a neural network operation is performed, the column circuit outputs a summation current to a bit line connected to the column circuit based on voltage applied to the plurality of word lines.

A ternary processing cell used as a memory cell and capable of in-memory arithmetic is disclosed which includes a first memory cell, adapted to hold a first digital value, a second memory cell, adapted to hold a second digital value, wherein a binary combination of the first digital value and the second digital value establishes a first ternary operand, a ternary input establishing a second ternary operand, and a ternary output, wherein the ternary output represents a multiplication of the first ternary operand and the second ternary operand.

Systems and methods are provided to improve traditional chip processing. Using crossbar computations, the convolution layer can be flattened into vectors, and the vectors can be grouped into a matrix where each row or column is a flattened filter. Each submatrix of the input corresponding to a position of a convolution window is also flattened into a vector. The convolution is computed as the dot product of each input vector and the filter matrix. Using intra-crossbar computations, the unused space of the crossbars is used to store replicas of the filters matrices and the unused space in XIN is used to store more elements of the input. In inter-crossbar computations, the unused crossbars are used to store replicas of the filters matrices and the unused XINs are used to store more elements of the input. Then, the method performs multiple convolution iterations in a single step.

The present invention discloses a computing array based on 1T1R device, operation circuits and operating methods thereof. The computing array has 1T1R arrays and a peripheral circuit; the 1T1R array is configured to achieve operation and storage of an operation result, and the peripheral circuit is configured to transmit data and control signals to control operation and storage processes of the 1T1R arrays; the operation circuits are respectively configured to implement a 1-bit full adder, a multi-bit step-by-step carry adder and optimization design thereof, a 2-bit data selector, a multi-bit carry select adder and a multi-bit pre-calculation adder; and in the operating method corresponding to the operation circuit, initialized resistance states of the 1T1R devices, word line input signals, bit line input signals and source line input signals are controlled to complete corresponding operation and storage processes.

An arithmetic apparatus according to an embodiment outputs a multiplicative value obtained by multiplying a weight value and an input value. The arithmetic apparatus includes a memristor, a logarithmic transform circuit, and a current-voltage converter circuit. The memristor is a device capable of changing voltage-current characteristic, and the memristor is preset to voltage-current characteristic according to the weight value. The logarithmic transform circuit applies an intermediate voltage, to the memristor, that is obtained by logarithmically transforming an input voltage according to the input value in accordance with a logarithmic transform function obtained by multiplying a natural logarithm function by a preset coefficient. The current-voltage converter circuit outputs an output voltage obtained by performing current-voltage conversion of current flowing through the memristor according to a preset linear function, as a multiplicative value.

A sum of products calculation circuit and a sum of products calculation method thereof are provided. A first input terminal of a differential amplifier is coupled to a reference voltage. A first adjustable resistance unit and a first parallel resistance unit are connected in parallel between a second input terminal of the differential amplifier and an operating voltage. A second adjustable resistance unit and a second parallel resistance unit are connected in parallel between the second input terminal of the differential amplifier and ground. A processing circuit adjusts resistance values of the first adjustable resistance unit and the second adjustable resistance unit, and calculates a sum of the products of a first input parameter and a second input parameter according to the resistance value of the second adjustable resistance unit corresponding to a situation in which an output of the differential amplifier is in transition.

A memory unit with a multiply-accumulate assist scheme for a plurality of multi-bit convolutional neural network based computing-in-memory applications is controlled by a reference voltage, a word line and a multi-bit input voltage. The memory unit includes a non-volatile memory cell, a voltage divider and a voltage keeper. The non-volatile memory cell is controlled by the word line and stores a weight. The voltage divider includes a data line and generates a charge current on the data line according to the reference voltage, and a voltage level of the data line is generated by the non-volatile memory cell and the charge current. The voltage keeper generates an output current on an output node according to the multi-bit input voltage and the voltage level of the data line, and the output current is corresponding to the multi-bit input voltage multiplied by the weight.

Disclosed is a cell that integrates a pixel and a two-terminal non-volatile memory device. The cell can be selectively operated in write, read and functional computing modes. In the write mode, a first data value is stored the memory device. In the read mode, it is read from the memory device. In the functional computing mode, the pixel captures a second data value and a sensed change in an electrical parameter (e.g., voltage or current) on a bitline connected to the cell is a function of both the first and second data value. Also disclosed is an IC structure that includes an array of the cells and, when multiple cells in a given column are concurrently operated in the functional computing mode, the sensed total change in the electrical parameter on the bitline for the column is indicative of a result of a dot product computation.