Quantitative calculation method and system for convolutional neural network

A convolutional neural network and calculation method technology, applied in the field of neural network algorithm hardware implementation, can solve problems such as low accuracy, large array power consumption, and insufficient computing power, so as to improve speed, reduce calculation power consumption, and increase The effect of throughput

Active Publication Date: 2020-04-10
HEFEI HENGSHUO SEMICON CO LTD
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Problems solved by technology

In the existing NOR Flash-based storage and computing integrated computing array, some control circuits are complex, resulting in high power consumption and insufficient computing power; some Flash unit threshold voltages are only divided into high an

Method used

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  • Quantitative calculation method and system for convolutional neural network
  • Quantitative calculation method and system for convolutional neural network
  • Quantitative calculation method and system for convolutional neural network

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Example Embodiment

[0084] Example 1

[0085] Such as Figure 7 In this embodiment, the AlexNet network is taken as an example to further illustrate the quantization calculation method of the convolutional neural network of the present invention:

[0086] The AlexNet network structure has a total of 8 layers, the first 5 layers are convolutional layers, and the next three layers are fully connected layers; the convolutional neural network quantitative calculation method accelerates the network into three steps:

[0087] The first step is to perform high-precision quantization on the first layer of convolution (Conv1), the second layer of convolution (conv2), the penultimate fully connected layer (Fc7), and the last fully connected layer (Fc8). Layer convolution (Conv3), fourth layer convolution (Conv4), fifth layer convolution (Conv5), 6th layer full connection (Fc6) for binarization, whether the accuracy of the software simulation simulation operation meets the requirements, For example, whether the a...

Example Embodiment

[0093] Example 2

[0094] Such as Picture 8 This embodiment takes the LeNet network as an example to further illustrate the quantization calculation method of the convolutional neural network of the present invention: the LeNet network is simple and only has 7 layers in total, including the convolutional layer (Conv), the pooling layer (pool) and the fully connected layer (Fc ), the convolutional neural network quantitative calculation method to accelerate the network includes three steps:

[0095] In the first step, the computationally intensive layers (convolutional layer, fully connected layer) are divided into binary quantization and high-precision quantization. Generally, the first layer of convolution (Conv1) and the last layer are fully The connection layer (Fc2) performs high-precision quantification, and the second layer convolution (Conv2) and the first layer fully connected layer (Fc1) are binarized and quantified. After the software simulation runs, whether the accurac...

Example Embodiment

[0101] Example 3

[0102] Such as Picture 9 In this embodiment, the DeepID1 network is taken as an example to further illustrate the quantization calculation method of the convolutional neural network of the present invention:

[0103] The DeepID1 neural network model used to extract facial features in face recognition algorithms is mainly composed of convolutional layer (Conv), pooling layer (pool) and fully connected layer (Fc). Convolutional neural network quantitative calculation method is accelerated The network consists of three steps:

[0104] In the first step, the computationally intensive layers (convolutional layer, fully connected layer) are divided into binary quantization and high-precision quantization. Generally, the first layer of convolution (Conv1) and the last layer are fully The connection layer (Fc) performs high-precision quantization, and the second layer of convolution (Conv2), the third layer of convolution (Conv3), and the fourth layer of convolution (Con...

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Abstract

The invention relates to the field of neural network algorithm hardware implementation, and discloses a quantitative calculation method and system for a convolutional neural network. The quantitativecalculation method comprises the steps of: allowing all calculation layers of a convolutional neural network to be respectively matched and quantized in a multi-valued quantification mode and a multi-bit quantification mode according to the calculation precision and calculation capability requirements, allowing the calculation layers after multi-bit quantification to be mapped to a high-precisionarray, and carrying out high-precision calculation; and mapping the calculation layers after multi-bit quantification to a high-calculation-power array, performing high-calculation-power calculation,and completing calculation of the convolutional neural network according to a high-precision calculation result and a high-calculation-power calculation result in combination with non-calculation layers. According to the invention, the reasoning speed of the convolutional neural network is increased; the accuracy is ensured; meanwhile, the network power consumption is reduced as much as possible;and high practical value and wide application prospect are achieved.

Description

technical field [0001] The invention relates to the technical field of neural network algorithm hardware implementation, in particular to a convolutional neural network quantization calculation method and system. Background technique [0002] Convolutional neural networks have shown great advantages in image recognition, object detection, and many machine learning applications. The convolutional neural network is mainly composed of a convolutional layer, a pooling layer, and a fully connected layer cascade. It mainly has the following operations, namely, the convolution operation between the pixel block and the convolution kernel, the activation operation for introducing nonlinearity, The downsampling operation (i.e., pooling) and full connection operation on the feature map to reduce the feature value. Among them, most of the calculations are in the convolutional layer and the fully connected layer. [0003] Large-scale convolutional neural networks have huge parameter se...

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Application Information

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IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045
Inventor 李政达任军郦晨侠吕向东盛荣华徐伟明徐瑞
Owner HEFEI HENGSHUO SEMICON CO LTD
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