DNN models have been proposed in many areas such as image classification, detection and segmentation. ![]() ![]() In machine learning, deep neural network (DNN) has shown great improvement over traditional algorithms. Challenges in the path are discussed to provide guidance for future work. ![]() We summarize how to design a technical route for practical applications based on these strategies. Then previous works on neural network acceleration are introduced following these topics. Based on the analysis, we generalize the acceleration strategies into five aspects-computing complexity, computing parallelism, data reuse, pruning and quantization. The architecture of networks and characteristics of FPGA are analyzed, compared and summarized, as well as their influence on acceleration tasks. In this paper, we research neural networks which are involved in the acceleration on FPGA-based platforms. However, the edge implementation of neural network inference is restricted because of conflicts between the high computation and storage complexity and resource-limited hardware platforms in applications scenarios. Neural network, which is one of representative applications of deep learning, has been widely used and developed many efficient models. The breakthrough of deep learning has started a technological revolution in various areas such as object identification, image/video recognition and semantic segmentation.
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