# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 7 Discussion and Future Work  
In this work, a new safety-critical and high-performance computing application for real-time AI-enhanced railway use has been introduced. Its design allows process isolation,  
Figure 5: Distance calculusto platform landmark using stereo vision camera (left (**a**) and right (**b**) images) and extracted depth map (**c**). Green frames represent detected landmark bounding box and the red frame the landmark’s corresponding area in depth map. This are is used for distance calculus.  
redundant execution, HW acceleration and abstraction making the platform compatible for most widely used AI inference techniques and AI model architecture and formats (including open standards such as ONNX).  
It is worth highlighting the implementation of specific HW and SW modules for the SELENE platform. A HW accelerator module, which can be customized to support specific data formats and neural network layers, has been deployed. HLSinf accelerator shows great performance on frequency agnostic evaluation. Using quantization and other AI model optimisation methods, the performance improves SoA. Its implementation presents a very high acceleration factor with respect to CPU execution for the Tiny-YOLOv4 algorithm. This work also presents the performance and accuracy evaluation of the use case functionalities over the SELENE platform, comparing it with executions carried out in the most widely used commercial HPC platforms, such as Nvidia's Jetson family boards or Xillix's FPGAs.  
In addition, a custom AI runtime and adapted inference SW, which abstracts the user application layer from platform specific HW configuration, has been carried out.  
Finally, the rootover and Jailhouse hypervisor implementations for RISC-V based system compatibility have also been successfully validated, making it possible to execute safety-related functionalities on the platform. This solution guarantees isolating executions of the different functionalities and allows the evaluation of redundant executions with voting system when needed.  
Regarding the use case, this work has demonstrated to be a valid HW platform for equipping autonomous trains that require real-time execution of safety (precision stop functionality) and non-safety (precision stop) functions based on CV and AI. With higher maturity, ASIC implementation and railway certification, the SELENE platform could suit railway industry requirements for both non-safety and safety level applications.  
The next steps of the investigation will focus on improving real time execution performance (reaching lower inference times) while keeping/increasing the detection accuracy. New NN architectures will be taken into account as candidates to port them into the SELENE board. OpenCV acceleration by HW should be implemented in order to speed up basic computer vision algorithms such as SGBM. On the other hand, they will also focus on more in-depth testing and validating the platform's possibilities for redundant execution (followed by different voting systems such as 2 oo3) in order to increase the safety level.  
Conceptualization, M.L. and J.F.; Methodology, M.L., J.F. and N.A.; Software, M.L., L.M. and F.E.; Validation, M.L., L.M. and F.E.; Formal analysis, M.L., L.M. and F.E.; Investigation, M.L., L.M., F.E., J.F. and N.A.; Writing--original draft, M.L.; Writing--review & editing, L.M., F.E. and N.A.; Supervision, J.F. and N.A.; Project administration, M.L. and J.F. All authors have read and agreed to the published version of this manuscript.  
The novel approach presented in this work is being developed as a specific railway use case for autonomous train operation into SELENE European research project. This project has received funding from RIA--Research and Innovation action under grant agreement No. 871467.  
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There is no available data.  
The author like to thank the whole PERception (PER) development team of CAF Signalling for their help and support.  
The authors declare no conflict of interest.