{"title":"Efficient FPGA-Based Convolutional Neural Network Implementation for Edge Computing","authors":"C. Pham-Quoc, T. N. Thinh","doi":"10.12720/jait.14.3.479-487","DOIUrl":null,"url":null,"abstract":"—In recent years, accelerating convolutional neural networks on Field Programmable Gate Array (FPGA) to improve the performance of the inference phase of artificial intelligent edge computing applications is a promising approach. This paper presents our proposed architecture for building a convolution neural network acceleration core on FPGA. The proposed FPGA-based core targets edge computing platforms where hardware resources and power efficiency are essential requirements. We use the MobileNet neural network model for image classification as a case study to evaluate our proposed system. We compare our work with a quad-core ARM Cortex processor at 1.2GHz and achieve speed-ups by up to 14.77 × convolution operators. Although our system is worse than a 6-core Intel Core i7 processor, it is more energy-efficiency than the Intel processor. Our proposed system is the best fit for edge computing.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12720/jait.14.3.479-487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1
Abstract
—In recent years, accelerating convolutional neural networks on Field Programmable Gate Array (FPGA) to improve the performance of the inference phase of artificial intelligent edge computing applications is a promising approach. This paper presents our proposed architecture for building a convolution neural network acceleration core on FPGA. The proposed FPGA-based core targets edge computing platforms where hardware resources and power efficiency are essential requirements. We use the MobileNet neural network model for image classification as a case study to evaluate our proposed system. We compare our work with a quad-core ARM Cortex processor at 1.2GHz and achieve speed-ups by up to 14.77 × convolution operators. Although our system is worse than a 6-core Intel Core i7 processor, it is more energy-efficiency than the Intel processor. Our proposed system is the best fit for edge computing.