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Memory module based on neural network, memory module and data processing method

A storage module and neural network technology, which is applied in the fields of storage modules, modules and data processing based on neural networks, can solve the problems of large processing time, data access cost, and increased computing processing time.

Active Publication Date: 2018-05-15
上海闪易半导体有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the process of neural network algorithm processing, a large number of matrix operations are included. At present, the calculation process is mainly realized through CPU (central processing unit) and memory. The CPU mainly performs core calculations, and accesses data from the memory in each operation. A large number of multiple data accesses greatly increase the time of the entire calculation process
And other parallel computing processor devices are also applied to neural networks, such as GPU (image processor) and TPU (tensor processor), etc. These devices can perform parallel computing, but data access still consumes a lot of processing time

Method used

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  • Memory module based on neural network, memory module and data processing method
  • Memory module based on neural network, memory module and data processing method
  • Memory module based on neural network, memory module and data processing method

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Experimental program
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Embodiment 1

[0076] refer to Image 6 As shown, in this embodiment, each storage unit includes a nonvolatile memory, the first direction is the row direction, and the second direction is the column direction. In the memory array, the first source and drain of each row of memory are Connected to the first electrical connection AL, the second source and drain of each column of memory are connected to the second electrical connection BL, the gate of each row of memory is connected to the third electrical connection CL, of course, the connection of the gate The manner may also be that the gate of each column of memory is connected to one electrical connection.

[0077] In the forward propagation, the first electrical connection AL can be used as the input terminal, and the second electrical connection BL can be used as the output terminal. Before matrix operation and propagation, data has been written in each memory and is in the first data state. When performing operations, the input signal ...

Embodiment 2

[0081] refer to image 3 As shown, in this embodiment, each storage unit includes a non-volatile memory and a MOS device, the two devices are connected in series, the first source and drain of the memory are connected to the second source and drain of the MOS device, and the first The direction is the row direction, and the second direction is the column direction. In the memory array, the first source and drain electrodes of each row of MOS devices are connected to the first electrical connection line AL, and the second source and drain electrodes of each row of memory devices are connected to the second The electric connection line BL, the gate of each row of memory is connected to the third electric connection line CL, and the gate of each column of MOS devices is electrically connected to the fourth electric connection line DL. Of course, the connection mode of the gate can also be as follows: The gates of a column of memory are connected to an electrical connection, and t...

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Abstract

The invention discloses a memory module based on a neural network. The memory module comprises a memory array formed by memory units of nonvolatile memories, wherein a source / drain electrode of one memory in a direction are electrically connected with a first electric connecting wire, the other source / drain electrode of the memory in the other direction are electrically connected with a second electric connecting wire, one of the first electric connecting wire and the second electric connecting wire is used for loading an input signal, and the input signal can be used for representing an inputvector numerical value of a middle layer of the neural network; and by virtue of the memory characteristics of the nonvolatile memory, memory data is an conductance value between two source / drain electrodes of the memories and a connecting weight numerical value, and then an output signal output by the other electric connecting wire is a numerical value after matrix calculation. Therefore, data signals in the neural network can be processed, transmitted and stored by virtue of the memory array, the data access time is not consumed, and meanwhile, the matrix operation scale and the treatment speed in the neural network can be effectively increased.

Description

technical field [0001] The invention relates to the field of neural network integrated circuit design, in particular to a neural network-based storage module, module and data processing method. Background technique [0002] Neural network (Neuron Network, NN) is an algorithmic mathematical model that imitates the behavior characteristics of animal neural networks and performs distributed parallel information processing. This algorithmic model is widely used in artificial intelligence fields such as speech recognition, image recognition, and automatic driving. [0003] In the neural network, depending on the complexity of the system, the purpose of processing information is achieved by adjusting the interconnection relationship between a large number of internal nodes. In the process of neural network algorithm processing, a large number of matrix operations are included. At present, the calculation process is mainly realized through CPU (central processing unit) and memory. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063
CPCG06N3/063
Inventor 张睿
Owner 上海闪易半导体有限公司