## 4 Discussion
These findings facilitate both the decision-making process related to the construction of a development environment and the selection of optimal hardware for system deployment, depending on the chosen neural network model and software framework. A certain limitation of the research is the constantly advancing technological development of graphics cards, but the comparative methodology for new products presented in the paper can be used for future analyses. In the near future, attention should be paid to modern CPUs such as the Intel Core Ultra 285K [77], equipped with integrated NPUs (neural processing units), which open up new possibilities in the field of data processing using artificial intelligence models. According to the manufacturer's assurances, these chips are compatible with frameworks such as OpenVINO or ONNX runtime, which can significantly increase the energy efficiency of computations while eliminating the need to purchase a dedicated graphics accelerator. Currently, Intel's processor is the only consumer desktop CPU that has an integrated NPU. Certainly, in the near future, more solutions of this type are expected to be introduced to the market, where the CPU will play a leading role in AI processing. Analogous chips supporting AI processing are already present in mobile devices, such as laptops (e.g., AMD Ryzen AI 9 HX 370, Intel Core Ultra 185H, and Qualcomm Snapdragon X Elite X1E) or mobile phones (e.g., MediaTek Dimensiony 9300, Google Tensor G4, and Qualcomm Snapdragon 8 Gen 3). Therefore, future research should focus on comparing the performance of integrated NPU-CPUs with graphics cards in terms of image processing for neural network vision systems. A key aspect of this analysis will be to determine the energy efficiency of the entire workstation, as changing the computing unit will also force modifications to other system components, making direct comparison difficult. An important role in further research will be played by tools designed for code migration between platforms [57], which will also allow for satisfactory performance without the need to redesign the code. The issue of performance portability may have a decisive impact on the implementation of new technological solutions in the near future. The lack of dedicated support for a framework or library can result in extremely slow performance, as seen for the AMD GPU in OpenCV. Migrating the neural network to another framework, in this case, is the only option to achieve the expected performance. A properly selected method should take into account both the properties of the hardware and the software included by the manufacturer, of which it is an integral part of the ecosystem. Measuring the efficiency of a GPU is significantly more difficult than a CPU precisely because of the need to take into account software designed for a specific chip manufacturer and sometimes even for a specific graphics card. The results of this analysis can contribute to a better understanding of hardware requirements and their impact on the development of machine vision systems used in navigation. The proposed benchmarking scheme can also be useful in evaluating future GPU solutions and adapted to dynamically changing GPU technologies, especially in the context of neural network training. The results of the research can support the process of choosing the right graphics card, taking into account both technical and economic aspects, such as the price-performance ratio. This analysis will be used both on smaller scales, such as single-user workstation configurations, and in larger projects based on multi-tenant workstation solutions. With the growing need for AI technology, we can expect an increase in demand for optimized hardware solutions that combine high computing performance with energy efficiency. In practice, the results of the research can be used to develop vision systems using deep learning technology for the automatic detection of navigational objects.