Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

A Survey of Accelerator Architectures for Deep Neural Networks

Recently,due to the availability of big data and the rapid growth of computing power,artificial intelligence(AI)has regained tremendous attention and investment.Machine learning(ML)approaches have been successfully applied to solve many problems in academia and in industry.Although the explosion of big data applications is driving the development of ML,it also imposes severe challenges of data processing speed and scalability on conventional computer systems.Computing platforms that are dedicatedly designed for AI applications have been considered,ranging from a complement to von Neumann platforms to a“must-have”and stand-alone technical solution.These platforms,which belong to a larger category named“domain-specific computing,”focus on specific customization for AI.In this article,we focus on summarizing the recent advances in accelerator designs for deep neural networks(DNNs)-that is,DNN accelerators.We discuss various architectures that support DNN executions in terms of computing units,dataflow optimization,targeted network topologies,architectures on emerging technologies,and accelerators for emerging applications.We also provide our visions on the future trend of AI chip designs.

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

Ultra-lightweight CNN design based on neural architecture search and knowledge distillation: A novel method to build the automatic recognition model of space target ISAR images

In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)images.ICH refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis.It is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the skull.Many computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels.However,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage costs.Further,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications.To overcome these problems,a fast and efficient system for the automatic detection of ICH is needed.We designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final predictions.The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection challenge.Our model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous decade.Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries.However,some diseases that are blocking the improvement in paddy production are considered as an ominous threat.Convolution Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.Nevertheless,the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge.This approach is time-consuming,and high computational resources are mandatory.In this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification.Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.

APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER

The dynamic working process of 52SFZ-140-207B type of hydraulic bumper isanalyzed. The modeling method using architecture-based neural networks is introduced. Using thismodeling method, the dynamic model of the hydraulic bumper is established; Based on this model thestructural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result showsthat the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamicperformance of the hydraulic bumper is improved through parameter optimization.

Side channel attacks for architecture extraction of neural networks

Side channel attacks(SCAs)on neural networks(NNs)are particularly efficient for retrieving secret information from NNs.We differentiate multiple types of threat scenarios regarding what kind of information is available before the attack and its purpose:recovering hyperparameters(the architecture)of the targeted NN,its weights(parameters),or its inputs.In this survey article,we consider the most relevant attacks to extract the architecture of CNNs.We also categorize SCAs,depending on access with respect to the victim:physical,local,or remote.Attacks targeting the architecture via local SCAs are most common.As of today,physical access seems necessary to retrieve the weights of an NN.We notably describe cache attacks,which are local SCAs aiming to extract the NN's underlying architecture.Few countermeasures have emerged;these are presented at the end of the survey.

A survey on computationally efficient neural architecture search

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally intensive.To solve this major limitation of NAS,improving the computational efficiency is essential in the design of NAS.However,a systematic overview of computationally efficient NAS(CE-NAS)methods still lacks.To fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities.The remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

AutoML: A systematic review on automated machine learning with neural architecture search

AutoML(Automated Machine Learning)is an emerging field that aims to automate the process of building machine learning models.AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied.In particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning.In this semantic review research,we summarize the data processing requirements for AutoML approaches and provide a detailed explanation.We place greater emphasis on neural architecture search(NAS)as it currently represents a highly popular sub-topic within the field of AutoML.NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task.We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark datasets.Additionally,we delve into several noteworthy research directions in NAS methods including one/two-stage NAS,one-shot NAS and joint hyperparameter with architecture optimization.We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed.To conclude,we examine several open problems(SOTA problems)within current AutoML methods that assure further investigation in future research.

Neural Network-Based Performance Index Model for Enterprise Goals Simulation and Forecasting

Enterprise Information System management has become an increasingly vital factor for many firms. Several organizations have encountered problems when attempting to evaluate organizational performance. Measurement of performance metrics is a key challenge for a huge number of firms. In order to preserve relevance and adaptability in competitive markets, it has become essential to respond proactively to complex events through informed decision-making that is supported by technology. Therefore, the objective of this study was to apply neural networks to the modeling, simulation, and forecasting of the effects of the performance indicators of Enterprise Information Systems on the achievement of corporate objectives and value creation. A set of quantifiable and sizeable conditionally independent associations were derived using a simplified joint probability distribution technique. Bayesian Neural Networks were utilized to describe the link between random variables (features) and to concisely and easily specify the joint probability distribution. The research demonstrated that Bayesian networks could effectively explore complex logical linkages by employing probability to represent uncertainty and probabilistic rules;and by applying impact models from Bayesian taxonomies to achieve learning and reasoning processes.

MRNDA:一种基于资源受限片上网络的深度神经网络加速器组播机制研究

片上网络(Network-on-Chip,NoC)在多处理器系统中得到了广泛的应用.近年来,有研究提出了基于NoC的深度神经网络(Deep Neural Network,DNN)加速器.基于NoC的DNN加速器设计利用NoC连接神经元计算设备,能够极大地减少加速器对片外存储的访问从而减少加速器的分类延迟和功耗.但是,若采用传统的单播NoC,大量的一对多数据包会极大的提高加速器的通信延迟.并且,目前的深度神经网络规模往往非常庞大,而NoC的核心数量是有限的.因此,文中提出了一种针对资源受限的NoC的组播方案.该方案利用有限数量的处理单元(Processor Element,PE)来计算大型的DNN,并且利用特殊的树形组播加速网络来减少加速器的通信延迟.仿真结果表明,和基准情况相比,本文提出的组播机制使加速器的分类延迟最高降低了86.7%,通信延迟最高降低了88.8%,而它的路由器面积和功耗仅占基准路由器的9.5%和10.3%.

图计算体系结构和系统软件关键技术综述

图计算作为分析事物之间关联关系的重要工具,近年来已成为各国政府及公司争夺的关键技术.学术界和工业界在图计算体系结构和系统软件关键技术方面取得了一定进展.然而,现实场景图计算大多具有动态变化、应用需求复杂多样等特征.这给图计算在基础理论、体系架构和系统软件关键技术方面提出了新的需求,同时也带来了新的挑战.为应对这些挑战,科研人员提出了一系列图计算系统或图计算加速器,通过高性能计算、并行计算等技术来优化图计算过程.综述国内外图计算体系结构和系统软件关键技术的研究发展现状,对国内外研究的最新进展进行归纳、比较和分析,并结合国家发展战略和重大应用需求,选取与我国国计民生密切相关的领域,从典型应用分析总结图计算相关技术的行业进展.最后,就未来的技术挑战和研究方向进行展望.

基于有偏采样的连续进化神经架构搜索

由于需要对每一个搜索到的架构进行独立的性能评估,神经架构搜索(NAS)往往需要耗费大量的时间和计算资源。提出一种基于有偏采样的连续进化NAS方法(OEvNAS)。OEvNAS在架构搜索过程中维护一个超网络,搜索空间中所有的神经网络架构都是该超网络的子网络。在演化计算的每一代对超网络进行少量的训练,子网络直接继承超网络的权重进行性能评估而无需重新训练。为提高超网络的预测性能,提出一种基于有偏采样的超网络训练策略,以更大的概率训练表现优异的网络,在减少权重耦合的同时提高训练效率。此外,设计一种新颖的交叉变异策略来提高算法的全局探索能力。在NATS-Bench和可微分架构搜索(DARTS)两个搜索空间上验证OEvNAS的性能。实验结果表明,OEvNAS的性能超越了对比的主流算法。在NATS-Bench搜索空间上,提出的超网络训练策略在CIFAR-10、CIFAR-100和ImageNet16-200上均取得了优异的预测性能;在DARTS搜索空间上,搜索到的最优神经网络架构在CIFAR-10和CIFAR-100上分别取得了97.67%和83.79%的分类精度。

深度学习在多核缓存预取中的应用研究综述

当前人工智能技术应用于系统结构领域的研究前景广阔,特别是将深度学习应用于多核架构的数据预取研究已经成为国内外的研究热点。针对基于深度学习的缓存预取任务进行了研究,形式化地定义了深度学习缓存预取模型。在介绍当前常见的多核缓存架构和预取技术的基础上,全面分析了现有基于深度学习的典型缓存预取器的设计思路。深度学习神经网络在多核缓存预取领域的应用主要采用了深度神经网络、循环神经网络、长短期记忆网络和注意力机制等机器学习方法,综合对比分析现有基于深度学习的数据预取神经网络模型后发现,基于深度学习的多核缓存预取技术在计算成本、模型优化和实用性等方面还存在着局限性,未来在自适应预取模型以及神经网络预取模型的实用性方面还有很大的研究探索空间和发展前景。

神经网络结构搜索在脑数据分析领域的研究进展

神经网络结构搜索(neural architecture search,NAS)是自动化机器学习的重要组成部分,已被广泛应用于多个领域,包括计算机视觉、语音识别等,能够针对特定数据、场景、任务寻找最优的深层神经网络结构.将NAS引入至脑数据分析领域,能够在图像分割、特征提取、辅助诊断等多个应用领域大幅度提升性能,展现低能耗自动化机器学习的优势.基于NAS进行脑数据分析是当前的研究热点之一,同时也具有一定挑战.目前,在此领域,国内外可供参考的综述性文献较少.对近年来国内外相关文献进行了细致地调研分析,从算法模型、研究任务、实验数据等不同方面对NAS在脑数据分析领域的研究现状进行了综述.同时,也对能够支撑NAS训练的脑数据集进行了系统性总结,并对NAS在脑数据分析中存在的挑战和未来的研究方向进行了分析和展望.

基于渐进式认知发现的新型配电网故障定位方法

在“双碳”目标下,持续接入分布式电源的新型配电网对运行可靠性提出了更高的要求,如何在渗透率变化的场景下提高现有故障定位方法的适应能力成为亟需解决的问题。为此,采用元学习特有的学会学习机制,提出了一种基于渐进式认知发现的新型配电网故障定位方法。首先,基于现有场景数据采用网络结构搜索算法构建当前场景个性化定位模型;然后,利用元学习算法提取模型构建过程中的知识因子,组成故障定位认知发现库;进而,在数据流和知识流的共同作用下,故障定位模型渐进地实现场景持续变化下的自主进化;最后,在PSCAD仿真平台对所提方法进行了验证。结果表明:所提方法具有定位精度高、鲁棒性强的优点,且在不同渗透率的故障场景下有着良好的泛化能力。研究结果可为基于人工智能的定位方法在实际系统中的应用提供技术支持。

基于神经网络架构搜索的细粒度花卉图像分类方法研究

为了提升深度卷积神经网络设计的自动化程度,并进一步提高细粒度花卉图像的分类准确率,提出了一种改进的基于DARTS的神经网络搜索方法,用于自动构建细粒度花卉图像分类模型。首先,通过构建注意力-卷积模块,形成全注意力-卷积搜索空间,增强网络对可判别特征的关注度。其次,通过构建具有更多浅层特征输入节点的密集连接缩减单元(DCR cell),保留更多的浅层特征信息,减少可判别特征信息的损失并促进多尺度特征融合。最后,在堆叠最佳cell时调整DCR cell的位置,构建参数量大小不一的网络模型,以便在更多的终端设备上部署。结果表明,该方法耗时4.5 h搜索到了最佳神经网络模型,在Oxford 102和Flower 17上的分类准确率分别为96.14%和94.12%。与AGNAS等方法相比,在Oxford 102上提高了1.40百分点,在Flower 17上提高了3.09百分点。