Proceedings of the 59th ACM/IEEE Design Automation Conference最新文献_第9页

LeHDC

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530593

Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu

引用次数: 10

DELTA δ

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530666

Nishant Gupta, M. S. Desai, M. Wijtvliet, Shubham Rai, Akash Kumar

引用次数: 0

SALO

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1163/1574-9347_dnp_e1028500

Guan Shen, Jieru Zhao, Quan Chen, Jingwen Leng, C. Li, Minyi Guo

引用次数: 1

Bipolar vector classifier for fault-tolerant deep neural networks 深度容错神经网络的双极向量分类器

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530498

Suyong Lee, Insu Choi, Joon-Sung Yang

{"title":"Bipolar vector classifier for fault-tolerant deep neural networks","authors":"Suyong Lee, Insu Choi, Joon-Sung Yang","doi":"10.1145/3489517.3530498","DOIUrl":"https://doi.org/10.1145/3489517.3530498","url":null,"abstract":"Deep Neural Networks (DNNs) surpass the human-level performance on specific tasks. The outperforming capability accelerate an adoption of DNNs to safety-critical applications such as autonomous vehicles and medical diagnosis. Millions of parameters in DNN requires a high memory capacity. A process technology scaling allows increasing memory density, however, the memory reliability confronts significant reliability issues causing errors in the memory. This can make stored weights in memory erroneous. Studies show that the erroneous weights can cause a significant accuracy loss. This motivates research on fault-tolerant DNN architectures. Despite of these efforts, DNNs are still vulnerable to errors, especially error in DNN classifier. In the worst case, because a classifier in convolutional neural network (CNN) is the last stage determining an input class, a single error in the classifier can cause a significant accuracy drop. To enhance the fault tolerance in CNN, this paper proposes a novel bipolar vector classifier which can be easily integrated with any CNN structures and can be incorporated with other fault tolerance approaches. Experimental results show that the proposed method stably maintains an accuracy with a high bit error rate up to 10−3 in the classifier.","PeriodicalId":373005,"journal":{"name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","volume":"510 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124467887","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: 1

R2B

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530521

Diansen Sun, Y. Chai, Chao Liu, Wei-Zen Sun, Qingpeng Zhang

引用次数: 1

TAIM TAIM

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530574

Nameun Kang, Hyungjun Kim, Hyunmyung Oh, Jae-Joon Kim

引用次数: 0

Designing critical systems with iterative automated safety analysis 设计具有迭代自动安全分析的关键系统

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530434

Ran Wei, Zhe Jiang, Xiaoran Guo, Haitao Mei, Athanasios Zolotas, T. Kelly

引用次数: 0

SMART: on simultaneously marching racetracks to improve the performance of racetrack-based main memory SMART:对赛马场同时行进，以提高赛马场为主内存的性能

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530538

Xiangjun Peng, Ming-Chang Yang, Ho Ming Tsui, Chi Ngai Leung, Wang Kang

引用次数: 0

SAPredictor SAPredictor

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530539

Yujuan Tan, Wei Chen, Zhulin Ma, Dan Xiao, Zhichao Yan, Duo Liu, Xianzhang Chen

引用次数: 0

AVATAR: an aging- and variation-aware dynamic timing analyzer for application-based DVAFS AVATAR:用于基于应用的DVAFS的老化和变化感知动态时序分析仪

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530530

Zuodong Zhang, Zizheng Guo, Yibo Lin, Runsheng Wang, Ru Huang

{"title":"AVATAR: an aging- and variation-aware dynamic timing analyzer for application-based DVAFS","authors":"Zuodong Zhang, Zizheng Guo, Yibo Lin, Runsheng Wang, Ru Huang","doi":"10.1145/3489517.3530530","DOIUrl":"https://doi.org/10.1145/3489517.3530530","url":null,"abstract":"As the timing guardband continues to increase with the continuous technology scaling, better-than-worst-case (BTWC) design has gained more and more attention. BTWC design can improve energy efficiency and/or performance by relaxing the conservative static timing constraints and exploiting the dynamic timing margin. However, to avoid potential reliability hazards, the existing dynamic timing analysis (DTA) tools have to add extra aging and variation guardbands, which are estimated under the worst-case corners of aging and variation. Such guardbanding method introduces unnecessary margin in timing analysis, thus reducing the performance and efficiency gains of BTWC designs. Therefore, in this paper, we propose AVATAR, an aging- and variation-aware dynamic timing analyzer that can perform DTA with the impact of transistor aging and random process variation. We also propose an application-based dynamic-voltage-accuracy-frequency-scaling (DVAFS) design flow based on AVATAR, which can improve energy efficiency by exploiting both dynamic timing slack (DTS) and the intrinsic error tolerance of the application. The results show that a 45.8% performance improvement and 68% power savings can be achieved by exploiting the intrinsic error tolerance. Compared with the conventional flow based on the corner-based DTA, the additional performance improvement of the proposed flow can be up to 14% or the additional power-saving can be up to 20%.","PeriodicalId":373005,"journal":{"name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904029","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: 5