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The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.

Neural networks (NN) for image processing in embedded systems expose two conflicting requirements: increasing computing power needs as models become more complex and constrained resource budget. In order to alleviate this problems, model compression based on quantization and pruning techniques are common. Derived models then need to fit on reconfigurable systems such as FPGAs for the embedded system to work properly. In this paper, we present HLSinf, an open source framework for the development of custom NN accelerators for FPGAs which provides efficient support to quantized and pruned NN models. With HLSinf, significant inference speedups can be obtained for typical medical image-based applications. In particular, we obtain up to 90x speedup factor when we combine quantization/pruning with the flexibility of HLSinf compared to CPU.

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At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.