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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