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In-memory multiplication and addition calculation method and device, chip and calculation equipment

A technology of adding calculations and products, applied in calculations, instruments, biological neural network models, etc., can solve the problems of operating performance bandwidth limitations, large resource consumption, etc., and achieve the effect of reducing resource occupation and power consumption

Pending Publication Date: 2021-12-03
NANJING HOUMO TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] For traditional neural network accelerators, a large amount of resources are consumed in each architectural link, such as the power consumption and delay of multipliers and adders, and its operating performance is also limited by the bandwidth between storage and processors.

Method used

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  • In-memory multiplication and addition calculation method and device, chip and calculation equipment
  • In-memory multiplication and addition calculation method and device, chip and calculation equipment
  • In-memory multiplication and addition calculation method and device, chip and calculation equipment

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Embodiment Construction

[0032]Hereinafter, exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited by the exemplary embodiments described here.

[0033] It should be noted that relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0034] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc. necessary logical sequence.

[0035] It should also be understood that in the embodiments of the present disclosure, "p...

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Abstract

The embodiment of the invention discloses an in-memory multiplication and addition calculation method and device, a chip and calculation equipment, and the method comprises the steps: obtaining a target number of input weight data pair sets from a preset neural network; generating codes respectively corresponding to the input weight data pairs in the input weight data pair set; storing the target number of input weight data pair sets into a preset first storage area; based on the code corresponding to each piece of product data in the second storage area, determining whether a corresponding target input weight data pair exists in the first storage area, and if yes, determining the product data as to-be-accumulated data corresponding to the target input weight data pair; and performing accumulation operation on the to-be-accumulated data respectively corresponding to the input weight data pairs in each input weight data pair set to obtain an accumulation result. According to the embodiment of the invention, the use of an addition tree is avoided, the resource occupation amount is reduced, the sparsity of data in the neural network can be utilized in the reverse search process, and the power consumption of accumulation operation is reduced.

Description

technical field [0001] The present disclosure relates to the technical field of computers, in particular to an in-memory multiply-add calculation method, device, chip and calculation equipment. Background technique [0002] A neural network, a computing system used to simulate the human brain to analyze and process information. It is the foundation of artificial intelligence, solving problems that would prove impossible or difficult to solve by human or statistical standards. Artificial neural networks are self-learning and can produce better results given more data. Neural network technology is widely used in face recognition, named entity recognition, speech recognition, signature verification, semantic detection and other scenarios. [0003] For traditional neural network accelerators, a large amount of resources are consumed in each architectural link, such as the power consumption and delay of multipliers and adders, and their operating performance is also limited by ...

Claims

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Application Information

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IPC IPC(8): G06F7/498G06N3/02
CPCG06F7/4983G06N3/02
Inventor 常亮李苇航司鑫沈朝晖陈亮吴强
Owner NANJING HOUMO TECH CO LTD