# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 2 Related Work
### Model Pruning and Quantization  
Since the popularization of deep convolutional neural networks, convolutional neural networks have gradually developed in the direction of making deeper and more complex network structures. By increasing the number of layers of the deep convolutional data network, stronger representation ability is obtained, which improves the accuracy and generalization ability of the model [28]. However, the complex network structure also brings problems such as large number of parameters, high computational complexity, and slow detection speed, and such deep models are difficult to deploy from bulky servers to embedded and mobile devices with limited computing power and memory space. Some researchers have discovered through studies of the human brain that neuronal synapses initially increase and subsequently decrease in individuals as they continue to develop. This phenomenon has given rise to the concept of pruning. Neural network pruning has demonstrated the ability to reduce model parameters by over 80% while significantly enhancing inference performance, with minimal impact on accuracy. [PERSON] et al. proposed the lottery hypothesis to find out the winning lottery tickets of large networks by pruning experiments on the weights of the smallest magnitude, and the winning ticket weights will return to the initial value before training, thus proving that the luck of the winning ticket comes from initialization [29]. [PERSON] et al. proposed a pruning method based on the L1 norm, and used the \(L1\) norm to prune unimportant weights. This approach does not introduce additional regularization, but has limitations due to the \(L1\) norm and is not applicable to some special weights [30]. [PERSON] et al. [31] propose a method to use model training to learn the connection and prune the model small weight join, which reduces the amount of storage required by AlexNet by 233.1 M without loss of accuracy. [PERSON] et al. proposed a simple and efficient pruning scheme that advances the scale factor in the batch normalized (BN) layer towards 0 by \(L1\) regularization, and prunes unimportant channels by assessing the importance of the \(\lambda\) parameters of the BN layer. This method has the characteristics of generalization accuracy, and a certain accuracy can be restored after fine-tuning the pruned model [32]. [PERSON] et al. proposed a method to use Taylor expansion to evaluate the influence of neurons on loss values, and then prune small neurons through continuous iterative pruning [33]. Model quantification came into interest in the 2010s. Model quantization can convert the floating-point algorithm of the neural network model into fixed-point numbers, and taking 8-bit quantization as an example, the 32-bit floating-point number model can be compressed by 75% without reducing the model accuracy [34]. Quantization can effectively accelerate model inference and is important for model deployment. [PERSON] et al. [35] approximated and simplified the model weights by binary representation of CNN model weights, and achieved comparable accuracy with standard networks in ImageNet classification while reducing floating-point number operations. [PERSON] et al. [31] proposed a three-stage model compression method in the technology of the predecessor technique: pruning, training quantization, and Hoffman coding, which reduced the number of model weight parameters by tens of times. In the network quantization and weight sharing section, the network model is compressed by reducing the number of bits required for weights. Their method reduces the total number of weights by connecting the same weights and fine-tuning the weights being shared. The \(4\times 4\) matrices in the upper left and lower right corners of Figure 2 represent the weight matrix and gradient matrix, respectively, and the four colors green, light green, yellow, and dark blue represent four different bins where the weights are quantified, and they share the same values for the weights of the same bins.