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Neural network processor board and related products

A neural network and processor technology, applied in the field of neural network processor boards and related products, can solve the problems of large amount of calculation and high power consumption, and achieve the advantages of small amount of calculation, low power consumption, saving transmission resources and computing resources. Effect

Active Publication Date: 2020-08-04
SHANGHAI CAMBRICON INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The operation of the existing neural network is based on CPU (Central Processing Unit, central processing unit) or GPU (English: Graphics Processing Unit, graphics processing unit) to realize the operation of neural network, the calculation amount of this kind of operation is large, and the power consumption is high

Method used

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  • Neural network processor board and related products
  • Neural network processor board and related products
  • Neural network processor board and related products

Examples

Experimental program
Comparison scheme
Effect test

no. 1 example

[0121] After the first mapping circuit receives the first input data (specifically, it can be the data block to be calculated sent by the main processing circuit, such as distribution data block or broadcast data block, etc.), the first mapping circuit can The data is processed to obtain the processed first input data and identify mask data associated with the first input data, the mask data is used to indicate whether the absolute value of the first input data is greater than a first threshold, such as 0.5, 0, etc. wait.

[0122] Specifically, when the absolute value of the first input data is greater than the first threshold, the input data is retained; otherwise, the first input data is deleted or set to 0. For example, the input matrix data block is The first threshold is 0.05, then the processed matrix data block can be obtained after being processed by the first mapping circuit The identification data block (also called mask matrix) associated with the matrix data bl...

no. 2 example

[0129] The first mapping circuit can be used to process the first input data and the second input data to obtain the processed first input data, the first identification mask data associated with the first input data, and the processed second input data. The input data and the second identification mask data associated with the second input data. Wherein, the first mask data or the second mask data is used to indicate whether the absolute value of the first or second input data is greater than a second threshold, and the second threshold is custom-set by the user side or the device side, such as 0.05, 0 and so on.

[0130] The processed first input data or second input data may be processed input data or unprocessed input data. For example, the first input data is a distributed data block, such as the matrix data block in the above example After being processed by the first mapping circuit, the processed distribution data block can be obtained, where the processed distribut...

no. 3 example

[0135] The first mapping circuit is not set in the main processing circuit, but the main processing circuit can send the third input data and the pre-stored third identification data associated with the third input data to the basic processing circuit connected to it middle. The basic processing circuit is provided with a second mapping circuit. A specific embodiment of the data compression process involved in the second mapping circuit is described below.

[0136] It should be understood that the third input data includes, but is not limited to, basic data blocks, partial broadcast data blocks, broadcast data blocks, and the like. Likewise, in the neural network processor, the third input data may also be at least one weight value, and / or at least one input neuron, which is not limited in this application.

[0137] In the second mapping circuit, the second mapping circuit may process the third input data according to the third identification data associated with the receive...

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Abstract

The invention provides a neural network processor board card and a related product. The neural network processor board card comprises a neural network chip packaging structure, a first electrical andnon-electrical connection device and a first substrate, the neural network chip packaging structure comprises a neural network chip, a second electrical and non-electrical connection device and a second substrate, the second substrate bears the neural network chip, and the second substrate is connected with the neural network chip through the second electrical and non-electrical connection device.The technical scheme provided by the invention has the advantages of small calculated amount and low power consumption.

Description

technical field [0001] The present disclosure relates to the field of neural networks, in particular to a neural network processor board and related products. Background technique [0002] Artificial Neural Network (ANN) is a research hotspot in the field of artificial intelligence since the 1980s. It abstracts the human brain neuron network from the perspective of information processing, establishes a simple model, and forms different networks according to different connection methods. In engineering and academia, it is often referred to directly as a neural network or a neural network. A neural network is an operational model consisting of a large number of nodes (or neurons) connected to each other. The calculation of the existing neural network is based on a CPU (Central Processing Unit, central processing unit) or a GPU (English: Graphics Processing Unit, graphics processing unit) to realize the calculation of the neural network, which requires a large amount of calcu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/063G06N3/08G06F9/50
CPCG06F9/5027G06N3/063G06N3/084Y02D10/00
Inventor 不公告发明人
Owner SHANGHAI CAMBRICON INFORMATION TECH CO LTD
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