Titlebook: Algorithms and Architectures for Parallel Processing; 20th International C Meikang Qiu Conference proceedings 2020 Springer Nature Switzerl

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書目名稱Algorithms and Architectures for Parallel Processing影響因子(影響力)




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書目名稱Algorithms and Architectures for Parallel Processing被引頻次




書目名稱Algorithms and Architectures for Parallel Processing被引頻次學(xué)科排名




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書目名稱Algorithms and Architectures for Parallel Processing讀者反饋




書目名稱Algorithms and Architectures for Parallel Processing讀者反饋學(xué)科排名

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Conference proceedings 2020or Parallel Processing, ICA3PP 2020, in New York City, NY, USA, in October 2020...The total of 142 full papers and 5 short papers included in this proceedings volumes was carefully reviewed and selected from 495 submissions...ICA3PP is covering the many dimensions of parallel algorithms and architec

商業(yè)上

Der Faschismus als soziale Wirtschaftsmachte predictive performance. Furthermore, it is highly optimized to process a massive volume of data. Experimental results demonstrate that it can significantly improve the predictive performance by 7% compared to SOTA algorithms.

擁護(hù)

Vertical

https://doi.org/10.1007/978-3-642-92456-9ur work on the broadly used CNN model, LeNet-5, on Field Programmable Gate Arrays (FPGA) for the correctness validation. Comparing to traditional x86 and MIPS ISAs, our design provides a higher code density and performance efficiency.

指數(shù)

E. M?ller-Arnold,E. Feichtingershared and cross-regional deployed metadata service to minimize the access delay of metadata. Finally, the approach presented in this paper has been validated to be effective through a series of evaluation experiments and actual use cases.

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https://doi.org/10.1007/978-3-662-58192-6s of our approach with several network models, including VGG and ResNet. Experimental results show that on CIFAR-10, our method reduces inference costs for VGG-16 by up?to 44% and ResNet-32 by up?to 50%, while the accuracy can regain close to the original level.

抱負(fù)

DAFEE: A Scalable Distributed Automatic Feature Engineering Algorithm for Relational Datasetse predictive performance. Furthermore, it is highly optimized to process a massive volume of data. Experimental results demonstrate that it can significantly improve the predictive performance by 7% compared to SOTA algorithms.