AMD and JR Kyushu have partnered to implement an AI-based solution using the AMD Kria K26 System-on-Module to automate track inspection, improving efficiency and safety for the Japanese bullet train operator.
- Improved efficiency and cost-effectiveness in track inspection process
- Enhanced accuracy in detecting track-related issues
- Potential for further enhancements and adaptability to changing conditions
AMD, the semiconductor company, has made an exciting announcement today regarding its partnership with JR Kyushu Railway Company (JR Kyushu), a important Japanese bullet train operator. JR Kyushu has implemented the AMD Kria K26 System-on-Module (SOM) to automate its track inspection process, marking a significant advancement in efficiency, cost-effectiveness, and accuracy.
Traditionally, inspecting miles of track on foot has been a time-consuming and labor-intensive task. However, with the introduction of this AI-based solution, JR Kyushu can now streamline its inspection procedures to meet Japan’s rigorous railway safety standards. Operating across an extensive network of over 1,455 miles of railroad tracks, with trains reaching speeds of up to 161 mph, safety is of paramount importance to JR Kyushu.
To enhance the accuracy and efficiency of track assessments, JR Kyushu opted for a solution powered by AMD and developed by Tokyo Artisan Intelligence (TAI). This solution utilizes high-speed image processing and advanced AI capabilities to detect and inspect loose bolts and other track-related issues. Kazuhiro Sakaguchi, deputy manager in the Engineering Division at JR Kyushu, expressed his satisfaction with the new solution, stating that it has significantly improved inspection efficiency and holds the potential for further enhancements.
At the core of this track inspection solution lies a vision computing box mounted on a cart that can inspect tracks at speeds of 12 mph. The box incorporates a high-speed camera equipped with the FPGA-based Kria K26 SOM for AI-enhanced pre-and-post data and image processing. The Kria K26 SOM is a compact embedded platform that integrates a custom-built AMD Zynq UltraScale+ MPSoC with DDR memory, nonvolatile storage devices, a security module, and an aluminum thermal heat spreader.
Hiroki Nakahara, co-founder and CEO of TAI, highlighted the cost reduction as the most significant benefit of implementing AI in this context. By replacing the conventional method of inspecting tracks on foot with carts, operational efficiency has witnessed a remarkable improvement.
Moreover, the AMD Kria SOM-powered solution stands out due to its programmability, durability, and embedded intelligence, making it adaptable to the unique and evolving conditions, geographies, and requirements of JR Kyushu’s expansive territory and ridership. Given that railways are situated within a natural environment, the ability to update the Kria SOM to accommodate the daily changing natural conditions is crucial and helps future-proof investments.
Chetan Khona, senior director of Industrial, Vision, Healthcare and Sciences Markets at AMD, emphasized the impact of Kria SOMs in driving innovation at the edge and simplifying solution development through deployment. The collaboration with JR Kyushu serves as a perfect example of how the limitless programmability of Kria SOMs, combined with edge AI computing, can automate processes and significantly enhance operational efficiency across various applications, ranging from machine vision to industrial robotics and AI/ML computing.
For more details on the AMD and JR Kyushu customer case study, please refer to the link provided.
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FPGA: Field Programmable Gate Arrays (FPGAs) are a type of technology used in the computer industry. They are integrated circuits that can be programmed to perform specific tasks. FPGAs are used in a variety of applications, including digital signal processing, networking, and embedded systems. They are also used in the development of artificial intelligence and machine learning algorithms. FPGAs are advantageous because they can be reprogrammed to perform different tasks, allowing for greater flexibility and faster development times. Additionally, FPGAs are more energy efficient than traditional processors, making them ideal for applications that require low power consumption.
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