Proceedings of the 59th ACM/IEEE Design Automation Conference最新文献_第10页

A near-storage framework for boosted data preprocessing of mass spectrum clustering 质谱聚类增强数据预处理的近存储框架

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530449

Weihong Xu, Jaeyoung Kang, T. Simunic

{"title":"A near-storage framework for boosted data preprocessing of mass spectrum clustering","authors":"Weihong Xu, Jaeyoung Kang, T. Simunic","doi":"10.1145/3489517.3530449","DOIUrl":"https://doi.org/10.1145/3489517.3530449","url":null,"abstract":"Mass spectrometry (MS) has been a key to proteomics and metabolomics due to its unique ability to identify and analyze protein structures. Modern MS equipment generates massive amount of tandem mass spectra with high redundancy, making spectral analysis the major bottleneck in design of new medicines. Mass spectrum clustering is one promising solution as it greatly reduces data redundancy and boosts protein identification. However, state-of-the-art MS tools take many hours to run spectrum clustering. Spectra loading and preprocessing consumes average 82% execution time and energy during clustering. We propose a near-storage framework, MSAS, to speed up spectrum preprocessing. Instead of loading data into host memory and CPU, MSAS processes spectra near storage, thus reducing the expensive cost of data movement. We present two types of accelerators that leverage internal bandwidth at two storage levels: SSD and channel. The accelerators are optimized to match the data rate at each storage level with negligible overhead. Our results demonstrate that the channel-level design yields the best performance improvement for preprocessing - it is up to 187X and 1.8X faster than the CPU and the state-of-the-art in-storage computing solution, INSIDER, respectively. After integrating channel-level MSAS into existing MS clustering tools, we measure system level improvements in speed of 3.5X to 9.8X with 2.8X to 11.9X better energy efficiency.","PeriodicalId":373005,"journal":{"name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","volume":"104 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":"123274153","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

Architecting DDR5 DRAM caches for non-volatile memory systems 为非易失性存储器系统设计DDR5 DRAM缓存

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530570

Xin Xin, Wanyi Zhu, Li Zhao

引用次数: 0

In-situ self-powered intelligent vision system with inference-adaptive energy scheduling for BNN-based always-on perception 基于bnn的实时感知推理自适应能量调度的原位自供电智能视觉系统

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530554

Maimaiti Nazhamaiti, Haijin Su, Han Xu, Zheyu Liu, F. Qiao, Qi Wei, Zidong Du, Xinghua Yang, Li Luo

引用次数: 0

HIMap HIMap

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530460

Xing Li, Lei Chen, Fan Yang, Mingxuan Yuan, Hongli Yan, Yupeng Wan

引用次数: 7

GraphRing GraphRing

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530571

Zerun Li, Xiaoming Chen, Yinhe Han

引用次数: 1

Hardware-efficient stochastic rounding unit design for DNN training: late breaking results 用于深度神经网络训练的硬件高效随机舍入单元设计:迟破结果

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530619

Sung-En Chang, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Z. Li, Yanyue Xie, Minghai Qin, Xue Lin, Zhenman Fang, Yanzhi Wang, Zhenman, Fang

引用次数: 0

HDPG

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530668

Yang Ni, Mariam Issa, Danny Abraham, M. Imani, Xunzhao Yin, M. Imani

{"title":"HDPG","authors":"Yang Ni, Mariam Issa, Danny Abraham, M. Imani, Xunzhao Yin, M. Imani","doi":"10.1145/3489517.3530668","DOIUrl":"https://doi.org/10.1145/3489517.3530668","url":null,"abstract":"Traditional robot control or more general continuous control tasks often rely on carefully hand-crafted classic control methods. These models often lack the self-learning adaptability and intelligence to achieve human-level control. On the other hand, recent advancements in Reinforcement Learning (RL) present algorithms that have the capability of human-like learning. The integration of Deep Neural Networks (DNN) and RL thereby enables autonomous learning in robot control tasks. However, DNN-based RL brings both high-quality learning and high computation cost, which is no longer ideal for currently fast-growing edge computing scenarios. In this paper, we introduce HDPG, a highly-efficient policy-based RL algorithm using Hyperdimensional Computing. Hyperdimensional computing is a lightweight brain-inspired learning methodology; its holistic representation of information leads to a well-defined set of hardware-friendly high-dimensional operations. Our HDPG fully exploits the efficient HDC for high-quality state value approximation and policy gradient update. In our experiments, we use HDPG for robotics tasks with continuous action space and achieve significantly higher rewards than DNN-based RL. Our evaluation also shows that HDPG achieves 4.7× faster and 5.3× higher energy efficiency than DNN-based RL running on embedded FPGA.","PeriodicalId":373005,"journal":{"name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","volume":"22 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":"121990820","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: 10

Hexagons are the bestagons: design automation for silicon dangling bond logic 六边形是标杆:设计自动化的硅悬垂键逻辑

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530525

Marcel Walter, S. S. H. Ng, K. Waluś, R. Wille

引用次数: 10

HDLock

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530515

Shijin Duan, Shaolei Ren, Xiaolin Xu

引用次数: 5

Multi-electrostatic FPGA placement considering SLICEL-SLICEM heterogeneity and clock feasibility 考虑SLICEL-SLICEM异构性和时钟可行性的多静电FPGA布局

Proceedings of the 59th ACM/IEEE Design Automation Conference Pub Date : 2022-07-10 DOI: 10.1145/3489517.3530568

Jing Mai, Yibai Meng, Zhixiong Di, Yibo Lin

引用次数: 6