## 1 Introduction
### Literature Review  
To the best of the authors' knowledge, ML networks have not been exclusively studied for computational efficiency's sake thus far in maritime studies. Even in recent carried out reviews such as [8], this issue has not been specifically addressed. The only attempt that partially addressed the computational efficiency is in [2], wherein the pace of computation has been considered as the assessment benchmarks sea state estimation models using the concept of wave buoy analogy (WBA). This shortcoming stems from the fact that the current literature has mainly utilized ML networks as an engineering tool for dealing with nonlinear problems, particularly high-order nonlinearities and stochastic patterns such as [9; 10]. The works in this scope are numerous, embracing a wide range of applications in seakeeping and maritime studies such as prediction of vessel motions in [11; 12] ship maneuvering [13; 14] which rested upon time-series forecasting models, Sea State Estimation (SSE) using vessel response data [15; 16; 17]. In fact, the carried-out research has endeavored to feed and train a network with batches of data, either experimental, real-world, or simulation data, to later utilize the network for prediction behavior. Additionally, the number of studies which presented a comparison of different networks or evaluated the performance of a network compared to the baseline networks or dataset are few. [PERSON] et al. 's proposed network has been compared with the baseline to highlight the efficiency of the deep learning model for estimating sea state using ship response data [18]. However, no discussion was drafted regarding computational efficiency, computational resources, and training time. Concerning these issues, a solid insight can be obtained through the set of studies conducted by [5] at MIT University. Although the studies have been carried out in computer science, the extension of the results embraces all applications of ML and Deep Learning (DL). [PERSON] et al. pronounced that the use of DL for applications with big data such as Natural Language Processing (NLP) and image recognition is indispensable [5]. However, despite the flexibility and efficiency, it comes with the complexity of the network such that for any improvement of performance by k factor, at least k\({}^{2}\) more parameters must be trained in the network. The second issue is overparameterization since increasing k\({}^{2}\) parameters end up with at least k\({}^{4}\) nodes, leading to overparameterization. This simply means that achieving a tenfold enhancement would necessitate a computation surge of at least 10,000 times. In [4], the limitation of ML and DL has been addressed in detail. Accordingly, several issues can be outlined. First, a slight increase in the number of inputs for a network exponentially soars the number of required nodes for training, so-called over-parameterization. In that, a state-of-the-art network for image recognition possessed 480 million parameters for a 1.2 million input training set from ImageNet. [PERSON] et al. extensively discussed the issues associated with training the Neural Networks (NL) which pronounced the impact of customized activation functions and regularization to improve training [19]. In terms of computational time, he stressed that it is directly related to \(2^{\text{t}}\) order with t as the network depth. However, the study was limited to a few specified scenarios. Computational resource-wise, although the GPUs and TPUs have grown incredibly to almost 10 times per year since 2012 [20], a significant portion of the rise originated from a less financially appealing avenue--extending model runtime across more machines. This fact highlights a challenge in scaling DL computation, as boosting hardware hours or the quantity of chips results in costs escalating nearly in tandem with computing power enhancements. Consequently, this approach could rapidly become financially unsustainable for real-world with big data.