Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Low-complexity convolutional neural network based on symbol random calculation

A convolutional neural network and random computing technology, applied in the field of convolutional neural networks, can solve the problems of reducing the complexity of hardware implementation, difficulty in small wearable devices, and high consumption of convolutional neural network hardware, so as to avoid calculation errors, The effect of reducing the number and reducing the delay

Pending Publication Date: 2019-12-10
SOUTHEAST UNIV
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: In order to solve the problem that the existing convolutional neural network hardware consumes so much that it is difficult to realize it on small wearable devices, it provides a low-complexity convolutional neural network architecture based on symbolic random computing, which can basically Under the premise of maintaining the same recognition accuracy of the neural network, the complexity of hardware implementation is effectively reduced

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Low-complexity convolutional neural network based on symbol random calculation
  • Low-complexity convolutional neural network based on symbol random calculation
  • Low-complexity convolutional neural network based on symbol random calculation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0064] This embodiment applies the concept and technology of random computing to the convolutional neural network. First, random computing is described here. Random computing is a random 0-1 bit stream proposed by Grain in 1969. "The probability of occurrence replaces the binary value to carry out the calculation method of data processing, and its advantage lies in that it can realize complex operation function with simple logic circuit.

[0065] Such as figure 1 As shown, through our research, we found that the traditional two random computing encoding methods are not suitable for direct application to convolution computing. First of all, for Unipolar Stochastic Computing (Unipolar Stochastic Computing, USC), because the value it represents can only be a positive number, and the weight of the filter in the neural network has both positive and...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a low-complexity convolutional neural network based on symbol random calculation. The system comprises a symbol random calculation module and an efficient convolution layer, apooling layer and a nonlinear excitation layer which are constructed based on the symbol random calculation module, the symbol random calculation module comprises a forward conversion module and a backward conversion module, the efficient convolution layer comprises a random processing unit and a random adder matrix, and the pooling layer is connected to the output end of the efficient convolutionlayer. According to the invention, a special sign bit is introduced; symbol random calculation is formed, the problem that the traditional random calculation can only represent positive numbers is solved; the calculation error caused by scaling coding is avoided, a novel parallel fast convolutional neural network architecture is provided based on the random calculation mode, the throughput rateof random convolution is increased while the hardware consumption is reduced, and the delay of random convolution calculation is reduced to a certain extent.

Description

technical field [0001] The invention belongs to the field of convolutional neural networks, in particular to a low-complexity convolutional neural network based on symbolic random calculation. Background technique [0002] Convolutional Neural Network (CNN) is the most important model in deep learning. In recent years, Convolutional Neural Network has been widely used in object detection, image classification, video surveillance and semantic segmentation. . Due to the high recognition accuracy of convolutional neural networks, it has been proved to be a powerful tool in the field of image recognition and sound recognition. In theory, as long as the number of layers of CNN is made deep enough, the network can obtain satisfactory recognition accuracy, and the CNN at this time becomes a deep convolutional neural network (Deep Convolutional Neural Network, DCNN). Since the operation of the deep convolutional neural network has the characteristics of intensive operation and sto...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045Y02D10/00
Inventor 张川王辉征尤肖虎
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products