An FPGA-based Convolution IP Core for Deep Neural Networks Acceleration

Xuan-Quang Nguyen, Cuong Pham-Quoc


The development of machine learning has made a revolution in various applications such as object detection, image/video recognition, and semantic segmentation. Neural networks, a class of machine learning, play a crucial role in this process because of their remarkable improvement over traditional algorithms. However, neural networks are now going deeper and cost a significant amount of computation operations. Therefore they usually work ineffectively in edge devices that have limited resources and low performance. In this paper, we research a solution to accelerate the neural network inference phase using FPGA-based platforms. We analyze neural network models, their mathematical operations, and the inference phase in various platforms. We also profile the characteristics that affect the performance of neural network inference. Based on the analysis, we propose an architecture to accelerate the convolution operation used in most neural networks and takes up most of the computations in networks in terms of parallelism, data reuse, and memory management. We conduct different experiments to validate the FPGA-based convolution core architecture as well as to compare performance. Experimental results show that the core is platform-independent. The core outperforms a quad-core ARM processor functioning at 1.2 GHz and a 6-core Intel CPU with speed-ups of up to 15.69× and 2.78×, respectively

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