Abstract:
A reversible logic circuit and an operation method thereof are provided. The logic circuit includes resistive switching cells, word lines, and bit lines. The word lines and the bit lines are perpendicular to each other. The anode of a resistive switching cell is connected to the word line as a first input terminal to apply logic operating voltage or be grounded. The cathode of a resistive switching cell is connected to the bit line as a second input terminal to apply logic operating voltage or be grounded. When performing reversible logic operation, four levels of resistance states of the resistive switching cell are used as logic outputs to implement single-input NOT and dual-input C-NOT reversible logic functions.
Abstract:
A processor including a computing and memory structure including X in number integration units and X in number communication units, and a control unit. The integration units are computing and memory units (CMUs), each computing and memory unit (CMU) is connected to a corresponding communication unit. The control unit is configured to produce control signals according to the commands, connect communication networks between the CMUs, choose operand addresses and result storage addresses, and search for one or a plurality of idle CMUs when extra CMUs are required for an operation. Each computing and memory unit includes M in number bit units and M−1 in number vertical line switches. Each bit unit includes a resistor, a horizontal line switch and N in number memristors. X is a positive integer greater than or equal to 2; M is a positive integer greater than or equal to 1.
Abstract:
A non-volatile Boolean logic operation circuit, including: two input ends; an output end; a first resistive switching element M1, the first resistive switching element M including a positive electrode and a negative electrode; and a second resistive switching element M2, the second resistive switching element M2 including a positive electrode and a negative electrode. The negative electrode of the first resistive switching element M1 operates as a first input end of the logic operation circuit. The negative electrode of the second resistive switching element M2 operates as a second input end of the logic operation circuit. The positive electrode of the second resistive switching element M2 is connected to the positive electrode of the first resistive switching element M1, and a connected end thereof operates as the output end of the logic operation circuit.
Abstract:
A joint short-time and long-time storage device, including a first electrode layer, a functional material layer connected to the first electrode layer, and a second electrode layer connected to the functional material layer. The first electrode layer is made of inert conductive metal, the second electrode layer is made of active conductive metal, and the functional material layer is made of chalcogenide.
Abstract:
The invention discloses a multiplier and an operation method based on 1T1R memory. The multiplier includes: a 1T1R crossbar A1, a 1T1R crossbar A2, a 1T1R crossbar A3, and a peripheral circuit. The 1T1R matrices are configured to realize operation and store result of it, and the peripheral circuit is configured to transfer data and control signals, thereby controlling the operation and storage process of the 1T1R matrices. An operation circuit is configured to respectively achieve NOR Boolean logic operations, two-bit binary multipliers, and optimization. The operation method corresponding to the operation circuit respectively completes the corresponding calculation and storage process by controlling an initialization resistance state of 1T1R devices, the size of a word line input signal, the size of a bit line input signal, and the size of a source line input signal.
Abstract:
An associative memory circuit including a first memristor, a second memristor, a fixed value resistor R, and an operational comparator. One terminal of the first memristor is a first input terminal of the associative memory circuit, and the other terminal of the first memristor is connected to a first input terminal of the operational comparator. One terminal of the second memristor is a second input terminal of the associative memory circuit, and the other terminal of the second memristor is connected to the first input terminal of the operational comparator. One terminal of the fixed value resistor is connected to the first input terminal of the operational comparator, and the other terminal of the fixed value resistor is connected to the ground. A second input terminal of the operational comparator is connected to a reference voltage.
Abstract:
The present disclosure belongs to the technical field of artificial neural networks, and provides to a max pooling processor based on 1T1R memory, comprising an input module, a max pooling operation module, and an output module; the input module is configured to transmit an operating voltage according to the convolution result in the convolutional neural network; the 1T1R memory in the max pooling operation module is configured to adjust a conductance value of the RRAM according to the gate voltage of the transistor therein to achieve the max pooling operation by using the non-volatile multi-value conductance regulation characteristic of the RRAM, and store a max pooling result; and the output module is configured to read the max pooling result and output it.
Abstract:
Disclosed by the disclosure is a convolutional neural network on-chip learning system based on non-volatile memory, comprising: an input module, a convolutional neural network module, an output module and a weight update module. The on-chip learning of the convolutional neural network module implements a synaptic function by using a characteristic which the conductance of a memristor changes according to an applied pulse, and the convolutional kernel value or synaptic weight value is stored in a memristor unit; the input module converts an input signal into a voltage signal required by the convolutional neural network module; the convolutional neural network module converts the input voltage signal level by level, and transmits the result to the output module to obtain an output of the network; and the weight update module adjusts the conductance value of the memristor in the convolutional neural network module according to the result of the output module to update a network convolutional kernel value or synaptic weight value.
Abstract:
The present disclosure discloses a memory-based CNN, comprising: an input module, a convolution layer circuit module, a pooling layer circuit module, an activation function module, a fully connected layer circuit module, a softmax function module and an output module, convolution kernel values or synapse weights are stored in the NOR FLASH units; the input module converts an input signal into a voltage signal required by the convolutional neural network; the convolutional layer circuit module convolves the voltage signal corresponding to the input signal with the convolution kernel values, and transmits the result to the activation function module; the activation function module activates the signal; the pooling layer circuit module performs a pooling operation on the activated signal; the fully connected layer circuit module multiplies the pooled signal with the synapse weights to achieve classification; the softmax function module normalizes the classification result into probability values as an output of the entire network. The disclosure satisfies the requirements of real-time data processing and has low hardware cost.
Abstract:
The present invention provides a preparation method of a bipolar gating memristor and a bipolar gating memristor. The preparation method includes: preparing a lower electrode; depositing a resistive material layer on the lower electrode; and depositing an upper electrode on the resistive material layer by using a magnetron sputtering manner to deposit the upper electrode, controlling upper electrode metal particles to have suitable kinetic energy by controlling sputtering power, controlling a vacuum degree of a region where the upper electrode and the resistive material layer are located, such that a redox reaction occurs spontaneously between the upper electrode and the resistive material layer during the deposition of the upper electrode to form a built-in bipolar gating layer; and continuously depositing the upper electrode on the built-in bipolar gating layer .