One Design And Implementation Of The Hardware System For Handwritten Digits Recognition Based On Convolutional Neural Network

Over the last several years,machine learning techniques,particularly when applied to neural networks,have played an increasingly important role in the design of pattern recognition systems.In fact,it could be argued that the availability of learning techniques has been a crucial factor in the recent success of pattern recognition applications such as continuous speech recognition and handwriting recognition.The traditional implements of convolutional neural network applications are based on general purpose processor.Note,however,that this approach can have some drawbacks,such as high cost and low speed,therefore make them cannot be widely used.If adopting the VLSI to implement these applications,it will sovle the above problems well.The main message of this paper is that one design and implementation of the hardware for handwritten digits recoginition,based on convolutional neural network,can be built by relying on convolutional neural network and VLSI,the primary works as follows in the paper:1.By using C Plus Plus programming language and software engineering related knowledge,a software simulation system is designed and implemented for handwritten digits recoginition to provide guidance for the following hardware system design,based on LeNet-5 convolutional neural network.2.According to the software simulation results,we determined the operand representation format in the hardware implementation,and then desined and implemented the basic computing units,in the end,optimized them.3.One hardware system for handwritten digits recognition is designed and implemented,by relying on LeNet-5 convolutional neural network and VLSI architecture design related technologies,based on the related design research in the laboratory.This is made possible by recent progress in machine learning and computer technology.This experiment can prove that,compared to software implementation,the hardware implementation of the convolutional neural network application for handwritten digits recognition has more excellent performance.