【作者】 胡飞；

【导师】 尤志强；

【作者基本信息】 湖南大学 ， 计算机科学与技术， 2017， 硕士

【摘要】 经过几十年的发展,人工神经网络在智能医疗,信号处理,自动控制等方面展现了其独特的优势,最为突出的进展是在深度学习领域。作为最成功的网络模型,卷积神经网络(CNN)可以从经过简单预处理的数据中学习到高阶的、抽象的、更接近本质的特征。但是越来越大的网络规模所需的计算量使得大部分智能设备无法承受。忆阻器在类脑计算方面具有的优良性能,引起了研究者的广泛关注。忆阻器的阻值可以根据流经本身的电荷量发生改变,因此具有学习和记忆功能。忆阻器还具有尺寸小、低功耗、模拟存储和非易失性等特性,非常适合应用于神经网络硬件实现。本文利用忆阻器与传统CMOS器件设计出了一个改进的忆阻器交叉阵列电路,可以更准确地存储权重与偏置,结合相应的编码方案后可以运算点积操作,并将其用于CNN中的卷积核与分类器部分。利用改进的忆阻器交叉阵列和基于CNN本身拥有的高容错性,该文还设计了一个忆阻卷积神经网络结构,可以完成一个基本CNN算法。在卷积操作后直接存储模拟形式的计算结果,使得卷积操作与池化操作之间避免了一次模数-数模转换过程。设计面积为0.8525cm2芯片上的运算性能是一台计算机速度的1770倍,在面积基本相当的前提下,运算性能是现有ISAAC电路的7.7倍。CNN运算过程中会产生大量需要存储的中间结果,针对这种现象本文提出了 一种新型的忆阻器交叉阵列—VMCA,该结构利用忆阻器特性来存储特征映射并实现局部接受域的向量化。基于VMCA设计CNN电路,节省了大量的数模转换器与模数转换器,并且使用很少的RAM。仿真实验表明,VMCA结构可以有效地应用于CNN电路中,与ISAAC电路相比,本文提出的设计可以在损失较小准确率的情况下,得到7倍的运算速度。更多还原

【Abstract】 The neural network has been developing quickly for recently decades,and its unique advantages in intelligent medical,signal processing,automatic control and many other research fields have been discovered.The most prominent progress is in the field of deep learning.As the most successful network model,convolutional neural network(CNN)could extract features,higher order,abstract and closer to nature,from simple preprocessing data.But the most of the intelligent device can’t provide the amount of calculation of more and more complex network model.Because memristor has excellent performance in the field of brain-like computing,memristor caused the wide attention of researchers.When a memristor is connected to a current source,the state of the memristor will change according to the amount of charge.As a result,memristor has the ability of learning and memory.In addition it has small size,low power,analog storage,non-volatile and other characteristics,so memristor is very suitable for hardware implementation of neural network system.In this thesis,we design an improved memristor crossbar array(MCA)to realize a CNN using memristors and CMOS devices.The MCA can store weights and bias accurately.A dot product between two vectors can be calculated after introducing an appropriate encoding scheme.The improved MCA is employed for convolution operations,and a classifier in a CNN.Then ’we also design a memristive CNN architecture using the improved MCA and based on the high fault-tolerance of CNNs to perform a basic CNN algorithm.In the designed architecture,the analog results of convolution operations are sampled and held before a pooling operation rather than using analog digital converters and digital analog converters between convolution and pooling operations in a previous architecture.Experimental results show the designed circuit with the area of 0.8525 cm2 can achieve a speedup of 1770× compared to a GPU platform.Compared with previous memristor-based architecture with a similar area,our design is 7.7× faster.There are many intermediate results will be produced in the computational process of CNN and need to be stored.In this thesis,we also design an improved MCA,called VMCA.VMCA is used to store feature map and implement vectorization of local receptive field.Based on VMCA,CNN circuit saves a lot of digital to analog converters and analog to digital converters,and uses very little RAM.Experimental results show that VMCA structure can be effectively applied to the CNN circuits.And the CNN circuit based on VMCA is 7x faster than previous design,but with few loss of precision.更多还原