Mixing precision quantification method of neural network

A quantification method, neural network technology, applied in biological neural network models, neural architectures, complex mathematical operations, etc., can solve problems such as unconsidered influence

Pending Publication Date: 2022-05-13
BEIJING JINGSHI INTELLIGENT TECH CO LTD
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Problems solved by technology

[0003] In addition, in the current method, if the quantization loss of a specific layer in the neural network is to be judged, only the status of the specific layer is considered, such as the loss of the output of the specific layer, the loss of the weight, etc., and the loss of the specific layer is not considered. The impact on the final result, so the current method cannot achieve the best balance between cost and prediction accuracy

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  • Mixing precision quantification method of neural network
  • Mixing precision quantification method of neural network
  • Mixing precision quantification method of neural network

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Embodiment Construction

[0024] Below in conjunction with accompanying drawing, structural principle and working principle of the present invention are specifically described:

[0025] Please refer to figure 1 , which shows a schematic diagram of a neural network NN according to an embodiment of the present invention. The neural network NN has a first layer L1, a second layer L2, and a third layer L3. The input of the first layer L1 is X1 and the output is X2, the input of the second layer L2 is X2 and the output is X3, and the input of the third layer L3 is X3 and the output is X4. That is to say, X2 is the output of the first layer L1 and the input of the second layer L2 at the same time, and X3 is the output of the second layer L2 and the input of the third layer L3 at the same time. Among them, X4 is the final output of the neural network NN, hereinafter referred to as the original final output. The neural network NN is a trained neural network and operates with a first precision. The first pr...

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Abstract

A hybrid precision quantization method for a neural network having a first precision and including a plurality of layers and an original final output. The mixed precision quantization method comprises the following steps of: performing quantization of a second precision on one of the layers and the input of the layer; obtaining the output of the layer according to the layer of the second precision and the input of the layer; performing inverse quantization on the output of the layer, and inputting the inverse quantized output of the layer to the next layer; obtaining a final output; obtaining a value of an objective function according to the final output and the original final output; repeating the steps until the value of the objective function corresponding to each of the layers is obtained; determining a quantization precision of each of the layers according to the value of the objective function corresponding to each of the layers; wherein the quantization precision is the first precision, the second precision, the third precision or the fourth precision.

Description

technical field [0001] The present invention relates to a mixed precision quantization method, and in particular to a neural network mixed precision quantization method. Background technique [0002] In the application of neural networks, the prediction process requires a lot of computing resources. Neural network quantization reduces computational cost, but may reduce prediction accuracy. Current quantization methods use the same precision to quantize the entire neural network, but this approach lacks flexibility. Moreover, most of the current quantization methods need to be matched with a large amount of labeled data and integrated into the training process to complete. [0003] In addition, in the current method, if we want to judge the quantization loss of a specific layer in the neural network, only the status of this specific layer is considered, such as the loss of the output of this specific layer, the loss of weight, etc., and this specific layer is not considered...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045G06N3/04G06F17/18G06N3/047
Inventor 赖俊宇
Owner BEIJING JINGSHI INTELLIGENT TECH CO LTD
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