2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)最新文献_第3页

Effective fault simulation of GPU’s permanent faults for reliability estimation of CNNs 对GPU永久故障进行有效的故障仿真，用于cnn的可靠性估计

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897823

Juan-David Guerrero-Balaguera, Robert Limas Sierra, M. Reorda

{"title":"Effective fault simulation of GPU’s permanent faults for reliability estimation of CNNs","authors":"Juan-David Guerrero-Balaguera, Robert Limas Sierra, M. Reorda","doi":"10.1109/IOLTS56730.2022.9897823","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897823","url":null,"abstract":"Convolutional Neural Networks (CNNs) and Graphic Processing Units (GPUs) are now increasingly adopted in many cutting edge safety-critical applications. Consequently, it is crucial to evaluate the reliability of these systems, since the hardware can be affected by several phenomena (e.g., wear out of the device), producing permanent defects in the GPU. These defects may induce wrong outcomes in the CNN that may endanger the application. Traditionally, the study of the effects of permanent faults on CNNs has been approached by resorting to application-level fault injection (e.g., acting on the weights). However, this approach has restricted scope, and it may not reveal the actual vulnerabilities in the GPU device. Hence, a more accurate evaluation of the fault effects is required, considering more in-depth details of the device’s hardware. This work introduces a more elaborated experimental evaluation of the impact of GPU’s permanent faults on the reliability of a CNN by resorting to a Software-Implemented Fault Injection(SWIFI) strategy, considering faults at the hardware level. The results of the fault simulation campaigns we performed on the GPU data-path cores are compared with those at the application level, proving that the latter ones are generally optimistic.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133558553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

SENAS: Security driven ENergy Aware Scheduler for Real Time Approximate Computing Tasks on Multi-Processor Systems SENAS:多处理器系统上实时近似计算任务的安全驱动的能量感知调度器

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897811

Krishnendu Guha, S. Saha, K. Mcdonald-Maier

{"title":"SENAS: Security driven ENergy Aware Scheduler for Real Time Approximate Computing Tasks on Multi-Processor Systems","authors":"Krishnendu Guha, S. Saha, K. Mcdonald-Maier","doi":"10.1109/IOLTS56730.2022.9897811","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897811","url":null,"abstract":"Present day real time approximate computing applications like image and video processing involves execution of a set of tasks before a certain amount of time or deadline. In addition to this, present day systems are associated with strict energy budget that cannot be changed post deployment. The tasks comprises of a mandatory and optional part. Completion of all mandatory portions of all tasks before deadline is much more important than result accuracy in such real time approximate computing applications. Based on the energy budget, the optional portions can be executed that determines the quality of service (QoS) of the system. In ideal scenario, sufficient energy budget is present that ensures completion of both mandatory and optional portions in a system with a pre-determined number of processors. However, if fault or malware attack occurs on one or more processors, then the system will cease to work and results may be fatal. In this work, we consider such a scenario where the processors may be faulty and stop functioning in post deployment phases or some malware may cause unexpected delays in processing or may cause unexpected power draining at runtime that will prevent the system from meeting its deadline. We propose a Security driven ENergy Aware Scheduler (SENAS) that works as a self aware agent. Initially, based on the available energy budget, SENAS determines which task is to be executed in which processor of a system. At runtime, SENAS constantly monitors the working of the processors and on detecting any anomaly in any of the processors, it reschedules its tasks at runtime by reducing execution of the optional portions of the tasks and ensuring completion before deadline with high QoS.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123506313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recovering Information on the CVA6 RISC-V CPU with a Baremetal Micro-Architectural Covert Channel 基于裸金属微架构隐蔽通道的CVA6 RISC-V CPU信息恢复

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897297

Valentin Martinoli, Y. Teglia, Abdellah Bouagoun, R. Leveugle

引用次数: 0

Soft Error Resilient Deep Learning Systems Using Neuron Gradient Statistics 基于神经元梯度统计的软误差弹性深度学习系统

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897815

C. Amarnath, Mohamed Mejri, Kwondo Ma, A. Chatterjee

{"title":"Soft Error Resilient Deep Learning Systems Using Neuron Gradient Statistics","authors":"C. Amarnath, Mohamed Mejri, Kwondo Ma, A. Chatterjee","doi":"10.1109/IOLTS56730.2022.9897815","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897815","url":null,"abstract":"Deep learning techniques have been widely adopted in daily life with applications ranging from face recognition to recommender systems. The substantial overhead of conventional error tolerance techniques precludes their widespread use, while approaches involving median filtering and invariant generation rely on alterations to DNN training that may be difficult to achieve for larger networks on larger datasets. To address this issue, this paper presents a novel approach taking advantage of the statistics of neuron output gradients to identify and suppress erroneous neuron values. By using the statistics of neurons’ gradients with respect to their neighbors, tighter statistical thresholds are obtained compared to the use of neuron output values alone. This approach is modular and is combined with accurate, low-overhead error detection methods to ensure it is used only when needed, further reducing its cost. Deep learning models can be trained using standard methods and our error correction module is fit to a trained DNN, achieving comparable or superior performance compared to baseline error correction methods while incurring comparable hardware overhead without needing to modify DNN training or utilize specialized hardware architectures.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124733778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Structural Test Generation for AI Accelerators using Neural Twins 基于神经双胞胎的人工智能加速器结构测试生成

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897773

Arjun Chaudhuri, Sanmitra Banerjee, K. Chakrabarty

引用次数: 0

All Digital Low-Cost Built-in Defect Testing Strategy for Operational Amplifiers with High Coverage 高覆盖运算放大器全数字低成本内置缺陷测试策略

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897224

Michael Sekyere, M. Saikiran, Degang Chen

{"title":"All Digital Low-Cost Built-in Defect Testing Strategy for Operational Amplifiers with High Coverage","authors":"Michael Sekyere, M. Saikiran, Degang Chen","doi":"10.1109/IOLTS56730.2022.9897224","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897224","url":null,"abstract":"Backed by standards like ISO26262, achieving near 100% defect coverage is becoming a common reliability requirement in the ever-growing automotive industry. However, achieving high defect coverage in an analog circuit has been proven to be a difficult/expensive task even with sophisticated analog and digital testing circuitry. In this work, we present a simple design for testability (DfT) technique that achieves 98% defect coverage for operational amplifiers including Widlar current reference and biasing circuitry. Our robust testing method utilizes purely digital testing circuits and is extremely time-efficient reducing the test cost. The proposed method can be used both at production test and for on-line health monitoring post-deployment to detect zero-time and latent defects. Also, the digital nature of our method presents a way for defect localization through the recorded bit streams. In this work, we also introduce a simple method to detect defects in the Widlar current reference and the bias current circuit. We validate all our results using extensive transistor-level simulations in UMC65nm technology.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"59 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115209990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Effective Hardware-Trojan Feature Extraction Against Adversarial Attacks at Gate-Level Netlists 针对门级网表对抗性攻击的有效硬件木马特征提取

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897557

Kazuki Yamashita, Tomohiro Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, N. Togawa

{"title":"Effective Hardware-Trojan Feature Extraction Against Adversarial Attacks at Gate-Level Netlists","authors":"Kazuki Yamashita, Tomohiro Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, N. Togawa","doi":"10.1109/IOLTS56730.2022.9897557","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897557","url":null,"abstract":"Recently, with the increase in outsourcing of IC design and manufacturing, the possibility of inserting hardware Trojans, which are circuits with malicious functions, has been pointed out. To prevent this threat, a method to identify hardware Trojans using neural networks has been proposed. On the other hand, adversarial attacks have emerged that modify circuit design information to reduce the accuracy of hardware-Trojan classification by neural networks. Since the features designed by existing methods do not take the attacks into account, it is necessary to consider a new method for countermeasures. In this paper, out of 76 features that are strongly related to hardware-Trojan features, we investigate them from the viewpoint of the robustness against the adversarial attacks on circuit design information and newly propose 24 hardware-Trojan features. We compare the classifiers using the proposed 24 features with the classifiers using 11, 36, 51, and 76 existing features, respectively and confirm that the proposed ones are more robust in identifying hardware Trojans in circuits subjected to the adversarial attacks.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the Impact of Cutting in Quantum Circuits Reliability to Transient Faults 理解量子电路中切割对瞬态故障可靠性的影响

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897308

Nadir Casciola, Edoardo Giusto, Emanuel A. Dri, Daniel Oliveira, P. Rech, B. Montrucchio

引用次数: 2

A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks 深入研究对深度神经网络的比特翻转攻击

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/IOLTS56730.2022.9897693

Kevin Hector, Mathieu Dumont, Pierre-Alain Moëllic, J. Dutertre

{"title":"A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks","authors":"Kevin Hector, Mathieu Dumont, Pierre-Alain Moëllic, J. Dutertre","doi":"10.1109/IOLTS56730.2022.9897693","DOIUrl":"https://doi.org/10.1109/IOLTS56730.2022.9897693","url":null,"abstract":"Deep neural network models are massively deployed on a wide variety of hardware platforms. This results in the appearance of new attack vectors that significantly extend the standard attack surface, extensively studied by the adversarial machine learning community. One of the first attack that aims at drastically dropping the performance of a model by targeting its parameters stored in memory, is the Bit-Flip Attack (BFA). In this work, we point out several evaluation challenges related to the BFA. First, the lack of an adversary’s budget in the standard threat model is problematic, especially when dealing with physical attacks. Moreover, since the BFA presents critical variability, we discuss the influence of some training parameters and the importance of the model architecture. This work is the first to present the impact of the BFA against fully-connected architectures that present different behaviors compared to convolutional neural networks. These results highlight the importance of defining robust and sound evaluation methodologies to properly evaluate the dangers of parameter-based attacks as well as measure the real level of robustness offered by a defense.","PeriodicalId":274595,"journal":{"name":"2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132460035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

IOLTS 2022 Foreword

2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2022-09-12 DOI: 10.1109/iolts56730.2022.9897687

引用次数: 0