An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Method and apparatus for distributed and cooperative computation in artificial neural networks

The invention discloses a memristor based image identification system and method. The system comprises an image signal extracting module, a plurality of memristor based neural network modules, and an identification module, wherein an input end of the identification module is connected with an output end of each neural network module, and an input end of each neural network module is connected with an output end of the signal extracting module. The method includes the following steps that (1) a feature vector of a gray level image to be identified is acquired and input into the neural network modules; (2) each neural network module grades and identifies the feature vector according to an image model of each neural network module; (3) a difference value between a value of each neural network module and obtained standard values during training is calculated, and the classification of an image to be identified is judged according to the difference value. The memristor based image identification system has good expansibility, high integration density, and low power consumption; the method has low time complexity and high identification accuracy.

Memristor based image identification system and method

The invention discloses a training device for a memristor-based neural network and a training method thereof. The neural network comprises N neuron layers which are connected one by one. The trainingmethod comprises the following steps of: inputting input data into a first neuron layer of the neural network so as to output an output result of the neural network at the Nth neuron layer, and calculating an output error of the Nth neuron layer; and counter-propagating the output error of the Nth neuron layer in a layer-by-layer manner so as to correct weight parameters between the neuron layers;and in the layer-by-layer counter-propagation process, three-valuing an output error of the mth neuron layer, and reversely inputting a voltage signal corresponding to an output result of the three-valuing operation to the mth neuron layer so as to correct a weight parameter of the mth neuron layer, wherein N is an integer greater than or equal to 3, and m is an integer greater than 1 and smallerthan N. According to the training method, the calculation ability of the memristor-based neural network is improved.

Training device for memristor-based neural network and training method thereof

The invention is suitable for the semiconductor integration circuit field and provides a circuit structure based on a memristor pulse nerve network. The circuit structure comprises a memristor unit and an output neuron unit. An output point of the memristor unit is connected to an input terminal of the output neuron unit. An identified image and a character are converted into a pulse electric signal. The pulse electric signal is used to change prevention of the memristor unit so as to realize weight updating of the pulse nerve network. Compared to a traditional nerve network circuit, a pulse nerve network circuit based on the memristor does not have an electric volatility disadvantage and possesses high precision; simultaneously, the memristor is a basic circuit component, individual storage is not needed so that weight transmission time is saved and precision is kept to be the same. A processing speed of the pulse nerve network circuit is increased and power consumption of the pulse nerve network circuit is reduced.

Circuit structure based on memristor pulse nerve network

The invention provides a three-valued neural network synaptic array comprising multiple electronic synapses and a control circuit The multiple electronic synapses form a mxn electronic synapse array The control circuit controls the resistance state of the multiple electronic synapses The number of the resistance state of the electronic synapses is 3 The invention further provides a neural morphology computing network based on the three-valued neural network synaptic array The number of the resistance state of the electronic synapses can be reduced under the premise of maintaining the computing accuracy so that the volume of the electronic synapses can be reduced, the technical difficulty of accurate adjustment of multiple resistance states can be eliminated and the stability of the electronic synapses can be enhanced

Three-valued neural network synaptic array and neural morphology computing network using three-valued neural network synaptic array

The invention discloses a feedback artificial neural network training method and a feedback artificial neural network calculating system and belongs to the field of calculation of neural networks. According to the artificial neural network training method, the synapse weight is adjusted according to a feedforward signal and a feedback signal at the two ends of each neural synapse; when the signals at the two ends of each neural synapse are an excitation feedforward signal and an excitation feedback signal respectively, the synapse weight is adjusted to the maximum value; when the signals at the two ends of each neural synapse are a tranquillization feedforward signal and an excitation feedback signal respectively, the synapse weight is adjusted to the minimum value. According to the feedback artificial neural network calculating system, each node circuit comprises a calculating module, a feedforward module and a feedback module and the node circuits are connected through the neural synapses simulated by memristors, and a series of pulse signals are adopted to achieve the feedback artificial neural network training method. An artificial neural network provided by the system and the method is high in rate of convergence, and the artificial neural network calculating system is few in control element, low in energy consumption and capable of being applied to data mining, pattern recognition, image recognition and other respects.

Feedback artificial neural network training method and feedback artificial neural network calculating system

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Feedback artificial neural network training method and feedback artificial neural network calculating system