The excellent performance of convolutional neural network makes it occupy an important position in the field of image processing.However, the actual application of the model mostly relies on GPU, which is difficult to deploy on power-sensitive embedded devices.In order to enable the model to be efficiently deployed on FPGA-based platforms, a fixed-point method for convolutional neural networks is proposed, which takes data accuracy and resource consumption as design indicators.Through the statistics of data distribution in the model and the division of data types, different fixed-point strategies are determined while the relationship among different quantification methods, overflow modes and hardware resource consumption is also given.Using Xilinx fixed-point library for testing, the experiment demonstrates that the use of 16-bit fixed-point number to quantify the model uniformly can reduce hardware resource consumption with a small loss of precision, and the hardware resource consumption is the same in different quantization modes, but it is a big difference in different overflow modes.
Key words
convolutional neural network /
fixed-point /
FPGA
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