CALCULATING device AND METHOD

A computing device and computing control technology, applied in the field of artificial intelligence, can solve the problems of reducing the computing speed of neural networks, increasing the hardware transmission bandwidth, etc., and achieve the effects of reducing power consumption overhead, increasing computing speed, and reducing overhead.

Pending Publication Date: 2019-02-15
SHANGHAI CAMBRICON INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The overhead of a large number of computing and storage resources will reduce the computing speed of the neural network. At th

Method used

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  • CALCULATING device AND METHOD

Examples

Experimental program
Comparison scheme
Effect test

specific example 1

[0183] Specific examples are as Figure 1N As shown, the weight data is 16-bit floating-point data, the sign bit is 0, the power bit is 10101, and the effective bit is 0110100000, so the actual value it represents is 1.40625*2 6 . The power neuron data sign bit is 1 bit, and the power bit data bit is 5 bits, that is, m is 5. The coding table is that when the power bit data is 11111, the corresponding power neuron data is 0, and when the power bit data is other values, the power bit data corresponds to the corresponding binary complement. If the power neuron is 000110, the actual value it represents is 64, which is 2 6 . The result of adding the power bit of the weight to the power bit of the power neuron is 11011, and the actual value of the result is 1.40625*2 12 , which is the product result of neurons and weights. Through this arithmetic operation, the multiplication operation becomes an addition operation, which reduces the amount of calculation required for calculati...

specific example 2

[0184] Specific example two such as Figure 1O As shown, the weight data is 32-bit floating-point data, the sign bit is 1, the power bit is 10000011, and the effective bit is 10000000000000000, so the actual value it represents is -1.5703125*2 4 . The power neuron data sign bit is 1 bit, and the power bit data bit is 5 bits, that is, m is 5. The coding table is that when the power bit data is 11111, the corresponding power neuron data is 0, and when the power bit data is other values, the power bit data corresponds to the corresponding binary complement. If the power neuron is 111100, the actual value it represents is -2 -4 . (The result of adding the power bit of the weight to the power bit of the power neuron is 01111111, and the actual value of the result is 1.5703125*2 0 , which is the product result of neurons and weights.

[0185] In step S1-3, the first power conversion unit converts the neuron data after the neural network operation into power neuron data.

[018...

example 1

[0359] Assuming that the data to be screened is a vector (1 0 101 34 243), and the components that need to be screened are less than 100, then the input position information data is also a vector, that is, a vector (1 1 0 1 0). The filtered data can still maintain the vector structure, and the vector length of the filtered data can be output at the same time.

[0360] Wherein, the position information vector can be input externally or generated internally. Optionally, the device in the present disclosure may further include a location information generation module, which may be used to generate a location information vector, and the location information generation module is connected to the data screening unit. Specifically, the position information generating module may generate a position information vector through vector operation, and the vector operation may be a vector comparison operation, that is, it is obtained by comparing the components of the vector to be screened ...

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Abstract

The invention provides an arithmetic device, comprising: an arithmetic control module for determining block information; The operation module is used for dividing, transposing and combining the operation matrix according to the block information to obtain the transposing matrix of the operation matrix. The present disclosure also provides an arithmetic method. The invention discloses an arithmeticdevice and a arithmetic method, which reduces the overhead of storage resources and computing resources and improves the arithmetic speed.

Description

[0001] This disclosure is a divisional application of the Chinese patent application number 201880001242.9, and the contents of the parent patent are all cited here. technical field [0002] The present disclosure relates to the technical field of artificial intelligence, and more particularly relates to a computing device and method. Background technique [0003] Multi-layer neural networks are widely used in tasks such as classification and recognition. In recent years, due to their high recognition rate and high parallelism, they have attracted extensive attention from academia and industry. [0004] At present, some neural networks with better performance are usually very large, which also means that these neural networks require a lot of computing resources and storage resources. The overhead of a large number of computing and storage resources will reduce the computing speed of the neural network. At the same time, the requirements for the transmission bandwidth of the...

Claims

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Application Information

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IPC IPC(8): G06N3/063
CPCG06N3/063G06F17/16G06T1/20G06T3/4046G06N3/048Y02D10/00G06F16/162G06F9/30025G06F9/30083G06F9/3802
Inventor 不公告发明人
Owner SHANGHAI CAMBRICON INFORMATION TECH CO LTD
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