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F-CNN: An FPGA-based framework for training Convolutional Neural Networks
2016
2016 IEEE 27th International Conference on Application-specific Systems, Architectures and Processors (ASAP)
This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying
doi:10.1109/asap.2016.7760779
dblp:conf/asap/ZhaoFLYWFMY16
fatcat:yx347ll6nnap5b3kgbbe3z3ucq