## 2 Related Work  
The concept of CNN originated in the 1960s. [PERSON] and [PERSON], two neuroscientists, discovered the information processing pattern in the biological visual system and thus proposed the concept of receptive fields, proving that the visual cortex is hierarchical, which is the theoretical basis of the convolutional neural network. In 1990, [PERSON] conducted supervised learning on Neocoginitro network, applied back propagation to the training process, and made subsequent improvements. In 1998, [PERSON] proposed an artificial neural network with a multi-layer structure, which is the famous LeNet-5 model [11]. In order to solve complex problems in various application scenarios, scientists have continued to improve and innovate CNN and put forward different models. As a typical representation of CNN, LeNet-5 contains the basic modules of CNN, namely, the convolution layer, pooling layer, activation layer, and full connection layer. The MNIST data set is a large handwritten character data set, including handwritten numeral images from zero to nine, with 60,000 training images and 10,000 test images. The image size is 28 \(\times\) 28 pixels, and is a single-channel grayscale image [12]. The CIFAR-10 data set is suitable for a small data set of universal object recognition and contains 10 categories of RGB color images with a size of 32 \(\times\) 32 pixels [13]. The 10 categories include airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. The dataset contains a total of 50,000 training images and 10,000 test images. In general, these two datasets are used to train various image processing systems and are widely used in the field of deep learning. As mentioned in the Introduction, neural networks are algorithmically complex and require huge computing power for training and inference. From the perspective of a light weight and flexibility, FPGAs, which have developed rapidly in recent years, have become increasingly integrated, including more and more on-chip RAMs, DSPs for arithmetic operations, and reconfigurable logic units [7]. When compared with GPUs, FPGA has the characteristics of high resource density, high performance-to-power ratio, and flexibility, so it is more suitable for harsh operating environments such as outer space [3]. From the perspective of performance and power consumption, FPGA provides superior performance/Watt over CPU and GPU, because FPGA's on-chip BRAMs, hard DSPs, and reconfigurable fabric allow for efficiently extracting fine-grained parallelisms from small/medium size matrices [14]. Despite the popularity of GPUs in deep learning implementations, FPGAs are emerging as an alternative mainly because of advantages such as their high performance per watt, parallelism, and their flexibility in model level optimizations when compared to the fixed architectures of GPUs [15]. In low-power environments, FPGAs are more suitable for mobile devices and resource-limited platforms. With the development of deep learning, the CNN model structure has become more and more complex, and it has become very difficult to conduct rapid validation and hardware-accelerated calculation for the model; at this stage, FPGA has come into the sight of AI scientists. Seeking new CNN hardware acceleration solutions has gradually become a hot topic in the field of AI [16; 17; 18]. More and more deep learning models use accelerators based on FPGAs. [PERSON] and [PERSON] developed fpgaConvNet, a new domain-specific modeling framework [19]. It implements an automatic design method for mapping ConvNets to a reconfigurable platform based on FPGA. When compared with the previous method, the hardware structure generated by this automatic design method can improve the performance density by 1.62 times. Based on Xilinx's SDAccel tool and FPGA, [PERSON] et al. implemented the Winograd convolution engine, which can be used with other layers running on host processors to run multiple popular CNNs [20].