Patent ID: 11861489
Assignee: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. The convolutional neural network on-chip learning system based on the non-volatile memory according to claim 1, characterized in that the fully connected layer circuit unit realizes a classification function, and is composed of the fully connected layer circuit composed of the memristor array and a softmax function part, weight mapping methods of the fully connected layer circuit unit and the convolutional layer circuit unit are different;
the fully connected layer circuit is configured to store and calculate a weight matrix, only completes a series of multiplication accumulation operations without shifting the weight matrix, two memristors are utilized as a synapse to realize positive and negative weight values, one end of the memristor is connected to the pooling layer circuit unit, and another end of the memristor is connected to the softmax function, the current of the same column is collected to obtain an output result of the layer, the output result is ml=Σl((Wkl+−Wkl−)*hk+bk),, z
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  ,, in the equation, hk in an input voltage pulse signal of a front synapse of the kth neuron node, Wkl+ and Wkl− are respectively the positive and negative synaptic weight values of the kth input of the lth neuron node stored in the memristor, and an effective weight value of the synapse is Wkl+−Wkl−, thereby realizing positive and negative synaptic weight values, bk is a bias term corresponding to the kth neuron node; ml represents an lth element output through the operation of the fully connected layer circuit; Σjemj is an exponential sum of all output signal elements; zl is a probability output value corresponding to the signal ml after being processed through the softmax function; and
the softmax function realizes, z
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  ,, that is, the function of normalizing the values output by the fully connected layer to a probability value, and then the result is transmitted to the output module to obtain an output of an entire convolutional neural network, and the result is sent to the weight update module.