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FD-SOI process-based calculation accelerator in binary convolutional neural network memory

A FD-SOI, convolutional neural network technology, applied in the field of binary convolutional neural network in-memory computing accelerators, to achieve the effects of high precision, reduced data transmission, and improved convolution processing speed

Active Publication Date: 2019-05-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] At present, there is no circuit based on FD-SOI technology to realize XOR processing of data to improve the convolution speed of CNN

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  • FD-SOI process-based calculation accelerator in binary convolutional neural network memory
  • FD-SOI process-based calculation accelerator in binary convolutional neural network memory
  • FD-SOI process-based calculation accelerator in binary convolutional neural network memory

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Embodiment Construction

[0048] The present invention will be described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0049]When studying the existing binary convolutional neural network, it is found that the convolutional neural network will use multiplication and addition when implementing the convolution process, and the multiplication calculation will greatly consume storage area, reduce operation speed and generate large power consumption , these shortcomings greatly reduce the performance index of the binarized convolutional neural network.

[0050] After analyzing the calculation structure of the existing binary convolutional neural network, it is found that the convolution algorithm of the binary convolutional neural network can be optimized to achieve the purpose of saving storage area, reducing computing power consumption and improving computing speed. The present invention proposes a convolution...

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Abstract

The invention belongs to the technical field of neural networks, and relates to an FD-SOI process-based calculation accelerator in binary convolutional neural network memory. The accelerator utilizesthe back gate voltage of the FD-SOI-MOSFET to adjust its threshold voltage to achieve XOR processing of the data. The convolution kernel parameters of the convolutional neural network are "one-dimensional" processed and stored in the memory, and the convolution process of the convolution kernel to the neural network is realized by XOR operation of the convolution kernel by the FD-SOI-MOSFET. Underthe premise of in-memory calculation, compared with the traditional convolution process, the XOR operation is completed by using the XOR operation while maintaining high precision, which greatly improves the convolution processing speed of the neural network and saves the neural network. Parameter storage space, data transfer, and reduced computing power.

Description

technical field [0001] The invention belongs to the technical field of neural networks, and relates to an FD-SOI process-based binary convolutional neural network in-memory computing accelerator. Background technique [0002] Convolutional Neural Network (CNN) is a common deep learning architecture inspired by the biological natural visual cognition mechanism (animal visual cortex cells are responsible for detecting optical signals), and is a special multi-layer feedforward Neural Networks. Its artificial neurons can respond to surrounding units within a part of the coverage area, which is excellent for large image processing. The main components of CNN are convolutional layer (Convolutional Layer), pooling layer (PoolingLayer) and full connection layer (Full Connection Layer). The purpose of the convolutional operation of the convolutional layer is to extract different features of the input. The first layer of convolution Layers may only be able to extract some low-level ...

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Application Information

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IPC IPC(8): G06N3/063G06N3/04
CPCY02D10/00
Inventor 胡绍刚刘爽邓阳杰罗鑫于奇刘洋
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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