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- W2888246238 abstract "Convolutional neural networks (CNNs) have been widely applied in many computer vision tasks due to its high accuracy. Meanwhile, various CNN accelerators based on FPGA platform have been proposed because they have advantages of high performance, reconfigurability, low-power consumption, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, it is challenging to deploy computation-intensive and memory-intensive CNNs on hardware implementations. In this paper, we have a comprehensive overview of existing FPGA technologies on accelerating CNNs and then outline underlying frameworks for mapping CNN models to energy-efficient FPGA. We adopted OpenCL hybrid systems to implement CNN accelerator using Xilinx’s KCU1500 board and demonstrated the better performance and resource utilization compared with existing work. This research traces the recent development trends and shows that the accelerator design space for CNNs on FPGAs can be further exploited." @default.
- W2888246238 created "2018-08-31" @default.
- W2888246238 creator A5074641628 @default.
- W2888246238 date "2018-08-23" @default.
- W2888246238 modified "2023-09-27" @default.
- W2888246238 title "Frameworks for Efficient Convolutional Neural Network Accelerator on FPGA" @default.
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- W2888246238 doi "https://doi.org/10.1007/978-981-10-8944-2_75" @default.
- W2888246238 hasPublicationYear "2018" @default.
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