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An FPGA-Based CNN Accelerator Integrating Depthwise Separable Convolution
The Convolutional Neural Network (CNN) has been used in many fields and has achieved remarkable results, such as image classification, face detection, and speech recognition. Compared to GPU (graphics processing unit) and ASIC, a FPGA (field programmable gate array)-based CNN accelerator has great advantages due to its low power consumption and reconfigurable property. However, FPGA's extremely limited resources and CNN's huge amount of parameters and computational complexity pose greatdoi:10.3390/electronics8030281 fatcat:mx4esrhr7zhmpfjd6gtbdsc3x4