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A Probabilistic Calculation-Based Artificial Neural Network Hardware Realization Device

An artificial neural network and hardware implementation technology, applied in the field of artificial neural network, can solve the problems of occupying connection resources, increasing power consumption, and the large scale of the hardware circuit of the neural network.

Active Publication Date: 2019-06-04
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In an integrated circuit, each logic gate needs to occupy a certain hardware area, and the large-scale network structure also occupies a large amount of connection resources, resulting in a very large scale of hardware circuits for the entire neural network, and very high implementation costs. On the other hand, the network Contains a large number of logic gates, which increases power consumption, and the existing technical solutions are difficult to solve

Method used

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  • A Probabilistic Calculation-Based Artificial Neural Network Hardware Realization Device
  • A Probabilistic Calculation-Based Artificial Neural Network Hardware Realization Device
  • A Probabilistic Calculation-Based Artificial Neural Network Hardware Realization Device

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Experimental program
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Embodiment 1

[0041] refer to figure 1 , a kind of artificial neural network hardware implementation device based on probability calculation in a preferred embodiment of the present invention, comprises input module, intermediate module and output module, and described input module comprises 1 input neuron (11), intermediate module It includes J interneurons (12), and the output module includes K output neurons (13), wherein, I, J, and K are all integers greater than or equal to 1. The input neuron (11) receives the first data (71) and outputs the first random data sequence (81). The interneuron (12) receives the first random data sequence (81) and the first random parameter sequence (51), and outputs the second random data sequence (82). The output neuron (13) receives the second random data sequence (82) and the second random parameter sequence (52), and outputs the second data (72). Among them, the first random data sequence (81), the second random data sequence (82), the first random ...

Embodiment 2

[0043] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0044] Each interneuron (12) can use the first random data sequence (81) as an input variable and the first random parameter sequence (51) as a function parameter to complete the radial basis function operation, and the operation process uses the probability number (that is, the probability of 0 or 1 appearing in the data sequence within a period of time represents a numerical value), after the operation, the second random data sequence ( 82 ) will be output as the output data of the intermediate neuron ( 12 ). The types of the radial basis functions include, but are not limited to, Gaussian functions, multi-quadratic functions, inverse multi-quadratic functions, thin-plate spline functions, cubic functions, and linear functions.

Embodiment 3

[0046] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0047] The first random data sequence (81), the second random data sequence (82), the first random parameter sequence (51), and the second random parameter sequence (52) can all be pseudo-random number sequences or true random number sequences. Its data width can be single-bit data width or multi-bit data width. Usually, the data in these sequences is one bit, that is, each data only needs one wire, which can greatly reduce the interconnection wires inside the network. However, in order to improve the calculation speed, these sequences can also use multi-bit data width to complete parallel calculations and speed up calculations.

[0048] Both the first random parameter sequence (51) and the second random parameter sequence (52) can be a sequence formed by a scalar parameter, or a sequence formed by a set of vector parameters. A scalar parameter means that the sequence rep...

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Abstract

The invention relates to an artificial neural network hardware implementation device based on probability calculation. The artificial neural network hardware implementation device based on probability calculation comprises input, intermediate and output modules. The input module is formed by I input neurons, and the input neurons receive first data and output a first random data sequence; the intermediate module is formed by J intermediate neurons, and the intermediate neurons receive the first random data / parameter sequence and output a second random sequence; and the output module is formed by K output neurons, and the output neurons receive the second random data / parameter sequence and output the second data, wherein I, J and K are integers greater than or equal to 1. The output end of the input neurons is connected with the input end of the intermediate neurons, the output end of the intermediate neurons is connected with the input end of the output neurons, and a complete or partial connection mode is adopted. The first and second random data sequences and the first and second random parameter sequences are expressed by the probability values of 0 or 1 appearing in the data sequences within a period of time. According to the neural network device, hardware logic and wiring resources can be greatly reduced, and circuit cost and power consumption can be reduced so that implementation of a super-large-scale neural network through small and medium-sized circuits is enabled to be possible.

Description

technical field [0001] The invention relates to the field of artificial neural networks, in particular to an artificial neural network hardware realization device based on probability calculation. Background technique [0002] Artificial neural network (ANN, referred to as neural network in this article) is an information processing system that simulates some functions of the human brain to a certain extent by referring to the structure of biological neural network (BNN) and the working mechanism of biological neurons, that is, simulating some functions of biological neurons. The basic function is to build artificial neurons with independent processing capabilities, and then train the neural network composed of a large number of artificial neurons, adjust the interconnection relationship between artificial neurons in the network, and make the network recognize the mapping relationship between input and output. Finally, the purpose of information processing is achieved. [0...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/06
Inventor 季渊陈文栋冉峰王雪纯王成其
Owner SHANGHAI UNIV
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