## 2 Related Work  
There are several methods that have been developed to make CNNs smaller, quicker, and more energy efficient. Here, we briefly discuss the related research on these CNN compression approaches.  
Pruning, widely recognized as the predominant method for model compression, decreases the model size by assigning zero values to specific weight components. According to various sparsity patterns of pruning, pruning methods can be classified as either unstructured or structured. The technique of unstructured pruning, as discussed in the work of [16; 17], has the potential to achieve significant compression ratios and accuracy. However, due to the unstructured feature of the sparsity pattern it produces, this strategy is unable to fully realize its theoretical acceleration on hardware systems. Structured pruning techniques, including filter [18; 19] and channel [20; 21] pruning, are solutions that are favorable to hardware. The performance of pruned models, however, limits the accuracy and compression ratio by the deliberately enforced structural pattern.  
Sparsification is the most widely used CNN compression method. A network's structure may be sparse at many levels, including the weight [22], filter [23], and channel [24]. A CNN model can generally be trained with sparsity-aware regularization [25] to achieve sparsity. In addition, the introduced sparsity regularization may be categorized as either structured or unstructured. Unstructured sparse models have shown excellent accuracy and compression ratio [26]. However, these models also introduce challenges such as irregular memory access and load issues of the hardware [27]. Structured sparse models are sometimes considered to be more suitable for hardware implementation due to their inherent architecture. However, it is worth noting that these models often exhibit worse compression ratios and accuracy compared to their unstructured counterparts [28].  
Quantization is a compression technique commonly used in CNN implementation in hardware, particularly for specialized CNN chips [29]. It utilizes a restricted number of bits for representing weight and activation [30; 31]. The choice of precision often depends on the available resource allocation and the desired level of accuracy. To achieve extremely low storage and computational costs, 1-bit weight has been proposed in [32] as a highly aggressive quantization approach.  
Tensor decomposition is a widely used technique in tensor theory, with the purpose of achieving compact representation of massive high-dimensional tensor-format data. Beginning with Tucker decomposition [10], numerous tensor decomposition techniques have been developed to compress CNNs by decomposing the original tensors into compact core tensors. In the studies of [33; 34; 35], decomposition methods of the tensor train (TT) [35], tensor ring (TR) [33], and hierarchical Tucker (HT) [34] have been utilized to obtain simple RNN architectures, resulting in a significant decrease in model parameters. The most recent study [36] introduces a low-rank Tucker-CP decomposition method. This method aims to enhance the approximation of weight tensors in convolutional layers, enabling effective CNN compression while preserving high accuracy.