Mohammad Pivezhandi, Phillip H. Jones, Joseph Zambreno
{"title":"并行事件直方图光流计算的FPGA实现","authors":"Mohammad Pivezhandi, Phillip H. Jones, Joseph Zambreno","doi":"10.1109/ASAP49362.2020.00038","DOIUrl":null,"url":null,"abstract":"In this paper, we present an FPGA-based architecture for histogram generation to support event-based camera optical flow calculation. Our proposed histogram generation mechanism reduces memory and logic resources by storing the time difference between consecutive events, instead of the absolute time of each event. Additionally, we explore the trade-off between system resource usage and histogram accuracy as a function of the precision at which time is encoded. Our results show that across three event-based camera benchmarks we can reduce the encoding of time from 32 to 7 bits with a loss of only approximately 3% in histogram accuracy. In comparison to a software implementation, our architecture shows a significant speedup.","PeriodicalId":375691,"journal":{"name":"2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"ParaHist: FPGA Implementation of Parallel Event-Based Histogram for Optical Flow Calculation\",\"authors\":\"Mohammad Pivezhandi, Phillip H. Jones, Joseph Zambreno\",\"doi\":\"10.1109/ASAP49362.2020.00038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present an FPGA-based architecture for histogram generation to support event-based camera optical flow calculation. Our proposed histogram generation mechanism reduces memory and logic resources by storing the time difference between consecutive events, instead of the absolute time of each event. Additionally, we explore the trade-off between system resource usage and histogram accuracy as a function of the precision at which time is encoded. Our results show that across three event-based camera benchmarks we can reduce the encoding of time from 32 to 7 bits with a loss of only approximately 3% in histogram accuracy. In comparison to a software implementation, our architecture shows a significant speedup.\",\"PeriodicalId\":375691,\"journal\":{\"name\":\"2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASAP49362.2020.00038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASAP49362.2020.00038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ParaHist: FPGA Implementation of Parallel Event-Based Histogram for Optical Flow Calculation
In this paper, we present an FPGA-based architecture for histogram generation to support event-based camera optical flow calculation. Our proposed histogram generation mechanism reduces memory and logic resources by storing the time difference between consecutive events, instead of the absolute time of each event. Additionally, we explore the trade-off between system resource usage and histogram accuracy as a function of the precision at which time is encoded. Our results show that across three event-based camera benchmarks we can reduce the encoding of time from 32 to 7 bits with a loss of only approximately 3% in histogram accuracy. In comparison to a software implementation, our architecture shows a significant speedup.