Wavelet transform compression and/or decompression-based neural network processing method

A neural network and processing method technology, applied in biological neural network models, electrical digital data processing, special data processing applications, etc. consumption, improve processing speed, and improve the effect of data quality

Active Publication Date: 2018-11-06
INST OF COMPUTING TECHNOLOGY - CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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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  • Wavelet transform compression and/or decompression-based neural network processing method
  • Wavelet transform compression and/or decompression-based neural network processing method
  • Wavelet transform compression and/or decompression-based neural network processing method

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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 invention provides a wavelet transform compression and / or decompression-based neural network processing method. The neural network processing method comprises the following steps of: compressing off-chip data and sending the off-chip data onto a chip; decompressing the off-chip data which is compressed and sent onto the chip; receiving the decompressed data and executing a neural network operation; compressing the data obtained via the neural network operation and sending the data off to the chip; and decompressing the data which is compressed and sent off to the chip and storing the dataas off-chip data, wherein the compression and / or compression carried out on the data is based on wavelet transform. According to the wavelet transform compression and / or decompression-based neural network processing method, data compression is carried out during data loading and storage, so that the IO quantity is decreased and the time and energy overheads are reduced.

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...

Claims

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Application Information

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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 TECHNOLOGY - CHINESE ACAD OF SCI
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