Chunkun Bo, V. Dang, Ted Xie, J. Wadden, M. Stan, K. Skadron
{"title":"可重构结构中的自动机处理","authors":"Chunkun Bo, V. Dang, Ted Xie, J. Wadden, M. Stan, K. Skadron","doi":"10.1145/3314576","DOIUrl":null,"url":null,"abstract":"We present a general automata processing framework on FPGAs, which generates an RTL kernel for automata processing together with an AXI and PCIe based I/O circuitry. We implement the framework on both local nodes and cloud platforms (Amazon AWS and Nimbix) with novel features. A full performance comparison of the proposed framework is conducted against state-of-the-art automata processing engines on CPUs, GPUs, and Micron’s Automata Processor using the ANMLZoo benchmark suite and some real-world datasets. Results show that FPGAs enable extremely high-throughput automata processing compared to von Neumann architectures. We also collect the resource utilization and power consumption on the two cloud platforms, and find that the I/O circuitry consumes most of the hardware resources and power. Furthermore, we propose a fast, symbol-only reconfiguration mechanism based on the framework for large pattern sets that cannot fit on a single device and need to be partitioned. The proposed method supports multiple passes of the input stream and reduces the re-compilation cost from hours to seconds.","PeriodicalId":162787,"journal":{"name":"ACM Transactions on Reconfigurable Technology and Systems (TRETS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automata Processing in Reconfigurable Architectures\",\"authors\":\"Chunkun Bo, V. Dang, Ted Xie, J. Wadden, M. Stan, K. Skadron\",\"doi\":\"10.1145/3314576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a general automata processing framework on FPGAs, which generates an RTL kernel for automata processing together with an AXI and PCIe based I/O circuitry. We implement the framework on both local nodes and cloud platforms (Amazon AWS and Nimbix) with novel features. A full performance comparison of the proposed framework is conducted against state-of-the-art automata processing engines on CPUs, GPUs, and Micron’s Automata Processor using the ANMLZoo benchmark suite and some real-world datasets. Results show that FPGAs enable extremely high-throughput automata processing compared to von Neumann architectures. We also collect the resource utilization and power consumption on the two cloud platforms, and find that the I/O circuitry consumes most of the hardware resources and power. Furthermore, we propose a fast, symbol-only reconfiguration mechanism based on the framework for large pattern sets that cannot fit on a single device and need to be partitioned. The proposed method supports multiple passes of the input stream and reduces the re-compilation cost from hours to seconds.\",\"PeriodicalId\":162787,\"journal\":{\"name\":\"ACM Transactions on Reconfigurable Technology and Systems (TRETS)\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Reconfigurable Technology and Systems (TRETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3314576\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Reconfigurable Technology and Systems (TRETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3314576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automata Processing in Reconfigurable Architectures
We present a general automata processing framework on FPGAs, which generates an RTL kernel for automata processing together with an AXI and PCIe based I/O circuitry. We implement the framework on both local nodes and cloud platforms (Amazon AWS and Nimbix) with novel features. A full performance comparison of the proposed framework is conducted against state-of-the-art automata processing engines on CPUs, GPUs, and Micron’s Automata Processor using the ANMLZoo benchmark suite and some real-world datasets. Results show that FPGAs enable extremely high-throughput automata processing compared to von Neumann architectures. We also collect the resource utilization and power consumption on the two cloud platforms, and find that the I/O circuitry consumes most of the hardware resources and power. Furthermore, we propose a fast, symbol-only reconfiguration mechanism based on the framework for large pattern sets that cannot fit on a single device and need to be partitioned. The proposed method supports multiple passes of the input stream and reduces the re-compilation cost from hours to seconds.