Neural network operational circuit based on digital-analog hybrid neuron

A computing circuit and neural network technology, applied in the field of neural networks, can solve the problems of difficult deployment of neural networks, high power consumption of neural networks, and difficulty in hardware implementation.

Inactive Publication Date: 2021-05-11
天津智模科技有限公司
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  • Abstract
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  • Claims
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Problems solved by technology

However, with the improvement of accuracy, the depth and parameter quantity of the neural network will also increase sharply. The existing technology cannot meet the condition of improving the accuracy ...

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  • Neural network operational circuit based on digital-analog hybrid neuron
  • Neural network operational circuit based on digital-analog hybrid neuron
  • Neural network operational circuit based on digital-analog hybrid neuron

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Embodiment Construction

[0021] The principles and features of the present invention will be described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0022] The neural network quantization algorithm provides great convenience for the terminal realization of the network. However, if 1-bit quantization is performed on both the weight and activation values ​​of the network at the same time, the accuracy of the network will be greatly lost; if multi-bit quantization is performed on both the weights and activation values ​​of the network at the same time, the multiplication and addition operations will increase dramatically. A convolutional neural network contains millions or even hundreds of millions of MAC operations, and the power consumption of so many MAC operations in traditional digital circuits is very high.

[0023] Therefore, there is an urgent need to i...

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Abstract

The invention discloses a neural network operational circuit based on digital-analog hybrid neurons, and relates to the technical field of neural networks. A multiplication operation circuit is used for carrying out multiplication operation on the quantization weight and the fixed-point quantization activation value, the multiplication operation circuit is realized by adopting N shift registers, wherein each shift register shifts and outputs m bits each time, and the addition operation circuit adopts an analog circuit to carry out wide vector summation operation on the m bits shifted and output by the shift registers in sequence, an AD conversion circuit is used for converting the wide vector summation result output by the convolution circuit into a digital signal, wherein the quantization weight is the exponential power of 2 or 0. The circuit is suitable for a mobile terminal and portable equipment, the precision of the neural network model is improved, the accuracy of the output result of the neural network model is improved, the chip area is reduced, the neural network model can be deployed on the terminal, the operation precision is ensured, the problems that a high-precision neural network is high in power consumption and hardware is difficult to implement are solved while precision improvement is met.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a neural network operation circuit based on a digital-analog mixed neuron. Background technique [0002] At present, with the development of neural network technology, in the fields of image processing and speech recognition, deep neural networks have achieved very good results in the cloud. Based on requirements such as latency, bandwidth, and privacy, it is necessary to push the neural network from the cloud to the terminal, and perform reasoning applications such as keyword detection, face recognition, and image classification on the terminal. However, with the improvement of accuracy, the depth and parameter quantity of the neural network will also increase sharply. The existing technology cannot meet the condition of improving the accuracy while overcoming the problem that the neural network consumes a lot of power and is difficult to implement. This leads to high-pr...

Claims

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Application Information

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IPC IPC(8): G06N3/06G06N3/063G06F9/30
CPCG06N3/061G06N3/063G06F9/30134
Inventor 张峰赵婷马春宇李淼
Owner 天津智模科技有限公司
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