European Stocks Rise as M&A Lifts Banco Sabadell and Spectris
The Stoxx Europe 600 Index was up 0.2% by 9:17 a.m. in Paris. The consumer and retail sectors were the best performers, while industrial shares lagged. Renewables stocks rose after a levy threatening the solar and wind industry was scrapped from the Senate GOP multitrillion-dollar tax bill.
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SSD MobileNet v2 Machine Vision AI Model Inferencing Test Results Show Axelera In The Lead YOLOv5s Machine Vision AI Model Results Shows The Axelera PCIe Card Wins Hands-Down But Nvidia Is ... More Competitive That said, Nvidia's higher-end RTX A4000 GPU maintained competitive performance in certain tests, particularly with smaller models like YOLOv5s. Hailo's M.2 module offered a compact, low-power alternative, though it trailed in raw throughput. Overall, the report illustrates that inference performance can vary significantly depending on the AI model and hardware pairing—an important takeaway for integrators and developers designing systems for specific image detection workloads. It also shows how dominant Axelera's Metis accelerators are in this very common AI inference application use case, versus major incumbent competitors like NVIDIA. Power consumption is an equally important factor, especially in AI edge deployments, where thermal and mechanical constraints and operational costs can limit design flexibility. Using per-frame energy metrics, our research found that all accelerators delivered improved efficiency over CPUs, with several using under one Joule per frame of inferencing. SSD MobileNet v2 Power Efficiency Results Shows Axelera Solutions Win In A Big Way YOLOv5s Power Efficiency Results Show Axelera Solutions Ahead But Nvidia And Hailo Close The Gap Here, Axelera's solutions out-performed competitors in all tests, offering the lowest energy use per frame in all AI models tested. NVIDIA's GPUs closed the gap somewhat in YOLO inferencing models, while Hailo maintained respectable efficiency, particularly for its compact form factor. The report highlights that AI performance gains do not always have to come at the cost of power efficiency, depending on architecture, models and workload optimizations employed. Beyond performance and efficiency, our report also looked at the developer setup process—an often under-appreciated element of total deployment cost. Here, platform complexity diverged more sharply. Axelera's SDK provided a relatively seamless experience with out-of-the-box support for multi-stream inference and minimal manual setup. Nvidia's solution required more hands-on configuration due to model compatibility limitations with DeepStream, while Hailo's SDK was Docker-based, but required model-specific pre-processing and compilation. The takeaway: development friction can vary widely between platforms and should factor into deployment timelines, especially for teams with limited AI or embedded systems expertise. Here Axelera's solutions once again demonstrated simplicity in its out-of-box experience and setup that the other solutions we tested could not match. Our study also analyzed object detection accuracy using real-world video footage. While all platforms produced usable results, differences in detection confidence and object recognition emerged. Axelera's accelerators showed a tendency to detect more objects and draw more bounding boxes across test scenes, likely a result of its model tuning and post-processing defaults that seemed more refined. Still, our report notes that all tested platforms could be further optimized with custom-trained models and threshold adjustments. As such, out-of-the-box accuracy may matter most for proof-of-concept development, whereas other, more complex deployments might rely on domain-specific model refinement and tuning. Axelera AI's Metis PCI Express Card And M.2 Module AI Inference Accelerators Our AI research and performance validation report underscores the growing segmentation in AI inference hardware. On one end, general-purpose GPUs like those from NVIDIA offer high flexibility and deep software ecosystem support, which is valuable in heterogeneous environments. On the other, dedicated inference engines like those from Axelera provide compelling efficiency and performance advantages for more focused use cases. As edge AI adoption grows, particularly in vision-centric applications, demand for energy-efficient, real-time inference is accelerating. Markets such as logistics, retail analytics, transportation, robotics and security are driving that need, with form factor, power efficiency, and ease of integration playing a greater role than raw compute throughput alone. While this round of testing (you can find our full research paper here) favored Axelera on several fronts—including performance, efficiency, and setup simplicity—this is not a one-size-fits-all outcome. Platform selection will depend heavily on use case, model requirements, deployment constraints, and available developer resources. What the data does make clear is that edge AI inference is no longer an exclusive market GPU acceleration. Domain-specific accelerators are proving they can compete, and in some cases lead, in the metrics that matter most for real-world deployments.
