Error evaluation method for multiple approximation technologies based on probabilistic graph model in special accelerator

A probabilistic graphical model, a technique of technical error, applied in directions based on specific mathematical models, probabilistic networks, computational models, etc., to solve problems that have not yet been considered

Pending Publication Date: 2021-05-14
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

Although several probabilistic approaches to approximate adders or exact scaling have recently been proposed for error estimation, t...

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  • Error evaluation method for multiple approximation technologies based on probabilistic graph model in special accelerator
  • Error evaluation method for multiple approximation technologies based on probabilistic graph model in special accelerator
  • Error evaluation method for multiple approximation technologies based on probabilistic graph model in special accelerator

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[0026] In order to make the technical means, creative features and effects of the present invention easy to understand, the following will describe the specific implementation in conjunction with the illustrations, and those skilled in the art can easily understand other advantages and effects of the present invention from the content described in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0027] Such as figure 1 As shown, the embodiment of the present invention provides a method for assessing various approximation technical errors based on a probability graphical model, and the method includes:

[0028] According to the predefined mapping rules, the data flow graph of a specified application is co...

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Abstract

The invention provides anerror evaluation method for multiple approximation technologies based on probabilistic graph model in special accelerator, wherein the approximation technology combination of any part is rapidly assessed and selected through the probability graph model, and on the premise that the output quality is guaranteed, the power consumption is reduced to the maximum extent. The method at least comprises the steps: converting a data flow diagram of a specified application into a Bayesian network of a probabilistic graph model according to a predefined mapping rule, the network comprising approximate configuration nodes in two states and input nodes in three states; then establishing an efficient and accurate dependency subgraph based on structure learning, and determining a conditional probability table of each node based on node parameter learning according to configuration of different approximation technologies; and according to a variable elimination inference algorithm, based on a Bayesian network and a conditional probability table, accurately solving a required marginal probability, and outputting an error distribution condition.

Description

technical field [0001] The invention relates to the technical field of a rapid evaluation method for multiple approximation techniques in a dedicated accelerator and the establishment of a probability graph model, in particular to an error assessment method for multiple approximation techniques based on a probability graph model in a dedicated accelerator. Background technique [0002] The power consumption problem of energy drives the emergence of new approximation technologies. By reducing the output quality of image processing, data mining, pattern recognition and other applications, approximate calculation, storage and communication can be greatly improved in terms of power consumption and performance. , in order to design for error-resistant application accelerators. This approximation technique can be applied to different components of a given application accelerator, but at different levels with different quality / power (or performance) tradeoffs. [0003] To maximize...

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Application Information

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IPC IPC(8): G06F30/27G06F30/17G06N7/00G06F111/08
CPCG06F30/27G06F30/17G06F2111/08G06N7/01
Inventor 李艳祥焦佳佳王立宝
Owner SHANGHAI MARITIME UNIVERSITY
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