Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design最新文献_第3页

ASPPLN

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3508352.3549456

Weihua Xiao, Weikang Qian

引用次数: 0

Quantum Machine Learning Applications in High-Energy Physics 量子机器学习在高能物理中的应用

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3508352.3561114

Andrea Delgado, Kathleen E. Hamilton

{"title":"Quantum Machine Learning Applications in High-Energy Physics","authors":"Andrea Delgado, Kathleen E. Hamilton","doi":"10.1145/3508352.3561114","DOIUrl":"https://doi.org/10.1145/3508352.3561114","url":null,"abstract":"Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.","PeriodicalId":367046,"journal":{"name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131443707","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

Session details: Designing DNN Accelerators 会议细节:设计DNN加速器

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3578437

Elliott Delaye, Yiyu Shi

引用次数: 0

Session details: Democratizing Design Automation with Open-Source Tools: Perspectives, Opportunities, and Challenges 会议细节:使用开源工具实现设计自动化的民主化:前景、机遇和挑战

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3578430

Antonino Tumeo

引用次数: 0

FastStamp

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3508352.3549357

Shehzeen Samarah Hussain, Nojan Sheybani, Paarth Neekhara, Xinqiao Zhang, J. Duarte, F. Koushanfar

{"title":"FastStamp","authors":"Shehzeen Samarah Hussain, Nojan Sheybani, Paarth Neekhara, Xinqiao Zhang, J. Duarte, F. Koushanfar","doi":"10.1145/3508352.3549357","DOIUrl":"https://doi.org/10.1145/3508352.3549357","url":null,"abstract":"Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are quickly replacing traditional hand-engineered pipelines. DNN based watermarking techniques have drastically improved the message capacity, imperceptibility and robustness of the embedded watermarks. However, this improvement comes at the cost of increased computational overhead of the watermark encoder neural network. In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware. We first propose a parameter efficient DNN model for embedding recoverable bit-strings in image pixels. Our proposed model can match the success metrics of prior state-of-the-art DNN based watermarking methods while being significantly faster and lighter in terms of memory footprint. We then design an FPGA based accelerator framework to further improve the model throughput and power consumption by leveraging data parallelism and customized computation paths. FastStamp allows embedding hardware signatures into images to establish media authenticity and ownership of digital media. Our best design achieves 68× faster inference as compared to GPU implementations of prior DNN based watermark encoder while consuming less power.","PeriodicalId":367046,"journal":{"name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122028160","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

MCQA MCQA

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3508352.3549462

Sunghye Park, Dohun Kim, Jae-Yoon Sim, Seokhyeong Kang

引用次数: 0

Session details: Hardware Security: Attacks and Countermeasures (Virtual) 硬件安全:攻击与对策(虚拟)

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3578445

J. Knechtel, L. Batina

引用次数: 0

Session details: Design for Low Energy, Low Resource, but High Quality 会议细节:低能量，低资源，但高质量的设计

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3578474

Ravikumar V. Chakaravarthy, Cong Hao

引用次数: 0

Re ² fresh 新鲜的

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3508352.3549345

Hyein Shin, Myeonggu Kang, Lee-Sup Kim

{"title":"Re\u0000 <sup>2</sup>\u0000 fresh","authors":"Hyein Shin, Myeonggu Kang, Lee-Sup Kim","doi":"10.1145/3508352.3549345","DOIUrl":"https://doi.org/10.1145/3508352.3549345","url":null,"abstract":"A severe read disturbance problem degrades the inference accuracy of a resistive RAM (ReRAM) based deep neural network (DNN) accelerator. Refresh, which reprograms the ReRAM cells, is the most obvious solution for the problem, but programming ReRAM consumes huge energy. To address the issue, we first analyze the resistance drift pattern of each conductance state and the actual read stress applied to the ReRAM array by considering the characteristics of ReRAM-based DNN accelerators. Based on the analysis, we cluster ReRAM cells into a few groups for each layer of DNN and generate a proper refresh cycle for each group in the oﬄine phase. The individual refresh cycles reduce energy consumption by reducing the number of unnecessary refresh operations. In the online phase, the refresh controller selectively launches refresh operations according to the generated refresh cycles. ReRAM cells are selectively refreshed by minimally modifying the conventional structure of the ReRAM-based DNN accelerator. The proposed work successfully resolves the read disturbance problem by reducing 97% of the energy consumption for the refresh operation while preserving inference accuracy.","PeriodicalId":367046,"journal":{"name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122297549","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

Session details: Compiler and System-Level Techniques for Efficient Machine Learning 会议细节:高效机器学习的编译器和系统级技术

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design Pub Date : 2022-10-30 DOI: 10.1145/3578427

S. Parameswaran, Martin Rapp

引用次数: 0