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Neural Network Processing Method Based on Wavelet Transform Compression and/or Decompression

A technology of neural network and wavelet transform, which is applied in the field of neural network processing system based on wavelet transform, can solve the problems of loading and storing data, such as long time and energy overhead, and memory access bottleneck, so as to reduce energy consumption, improve processing speed, improve Data Quality Effects

Active Publication Date: 2022-02-25
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] Various current neural network computing devices often face the problem of memory access bottlenecks, and loading and storing data causes a lot of time and energy overhead

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  • Neural Network Processing Method Based on Wavelet Transform Compression and/or Decompression
  • Neural Network Processing Method Based on Wavelet Transform Compression and/or Decompression
  • Neural Network Processing Method Based on Wavelet Transform Compression and/or Decompression

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Embodiment Construction

[0036] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0037] In order to solve the problem of memory access bottlenecks faced by various existing neural network computing devices, and reduce the time and energy overhead caused when loading and storing data, this disclosure uses wavelet transform to compress data, specifically, wavelet basis functions can be used to The input / output data is subjected to wavelet transform to compress the data.

[0038] Wavelet transform (wavelet transform, WT) is a transformation analysis method, which inherits and develops the idea of ​​short-time Fourier transform localization, and at the same time overcomes the shortcomings of the window size not changing with frequency, and can provide a "time window" that changes with frequency. -Freq...

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Abstract

The present disclosure provides a neural network processing method based on wavelet transform compression and / or decompression, wherein the neural network processing method includes: compressing off-chip data and sending it to the chip; compressing and sending the data to the chip Decompressing the data; receiving the decompressed data and performing neural network operations; compressing the data obtained by the neural network operations and sending them off-chip; and decompressing the compressed and sent off-chip data and stored as off-chip data; where the compression and / or decompression operations are performed on the data based on wavelet transform. The disclosed neural network processing method based on wavelet transform compression and / or decompression reduces the amount of IO and reduces time and energy costs by performing data compression when loading and storing data.

Description

technical field [0001] The disclosure belongs to the field of computer technology, and more specifically relates to a neural network processing system and method based on wavelet transform. Background technique [0002] Artificial Neural Networks (ANNs) are referred to as Neural Networks (NNs) for short. It is an algorithmic mathematical model that imitates the behavior characteristics of animal neural networks and performs distributed parallel information processing. This kind of network depends on the complexity of the system, and achieves the purpose of processing information by adjusting the interconnection relationship between a large number of internal nodes. The concept of deep learning (deep learning) originated from the research of artificial neural networks. A multi-layer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representation attribute categories or features t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02G06F17/14G06F13/28
CPCG06F13/28G06F17/148G06N3/02Y02D10/00
Inventor 张潇金禄旸张磊陈云霁
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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