Why Dutch Pensions Overhaul Will Reverberate in Swaps Market
The Dutch occupational pension system is the largest in the euro area, with almost €1.8 trillion ($2.1 trillion) of assets under management as of the first quarter of 2025. It's embarking on a complex transition from so-called defined-benefit pensions to a new system involving defined contributions.
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Forbes
40 minutes ago
- Forbes
Axelera AI Accelerators Smoke Competitors In Machine Vision Research Study
Axelera CEO Fabrizio Del Maffeo Holds The Company's PCIe AI Accelerator As AI-accelerated workloads proliferate across edge environments—from smart cities to retail and industrial surveillance—choosing the right inference accelerator has become a mission-critical decision for many businesses. In a new competitive benchmark study conducted by our analysts at HotTech Vision and Analysis, we put several of today's leading edge AI acceleration platforms to the test in a demanding, real-world scenario: multi-stream computer vision inference processing of high-definition video feeds. The study evaluated AI accelerators from Nvidia, Hailo, and Axelera AI across seven object detection models, including SSD MobileNet and multiple versions of YOLO, to simulate a surveillance system with 14 concurrent 1080p video streams. The goal was to assess real-time throughput, energy efficiency, deployment complexity and detection accuracy of these top accelerators, which all speak to a product's overall TCO value proposition. Measuring AI Accelerator Performance In Machine Vision Applications All of the accelerators tested provided significant gains over CPU-only inference—some up to 30x faster—underscoring how vital dedicated hardware accelerators have become for AI inference. Among the tested devices, PCIe and M.2 accelerators from Axelera showed consistently stronger throughput across every model, especially with heavier YOLOv5m and YOLOv8l workloads. Notably, the Axelera PCIe card maintained performance levels where several other accelerators tapered off, and it consistently smoked the competition across all model implementations tested. 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.
Bloomberg
an hour ago
- Bloomberg
FICO Drops, CoreWeave Dips, Big Banks Slide
On this episode of Stock Movers: - Fair Isaac (EFX), better known as FICO, saw shares headed for its worst slide since March 2020, after federal regulators said government-sponsored mortgage entities Fannie Mae and Freddie Mac will be able to use a second firm when determining borrowers' creditworthiness. Federal Housing Finance Agency Director Bill Pulte said in a post on X that Fannie and Freddie will now allow lenders to accept the Vantage 4.0 credit model to 'increase competition' in the credit score said the move should expand credit access to millions of potential borrowers living in rural areas and bring down closing costs. Since joining the FHFA, Pulte has pledged to do 'a full scale review' of all credit bureaus. He also suggested that FICO should focus on being more economical in their pricing. - CoreWeave (CRW) shares dipped after it was downgraded to neutral-equivalent ratings at Stifel and Mizuho, following the company's all-stock acquisition of data-center operator Core Scientific Inc. The Cloud infrastructure provider was also initiated with a hold rating at CFRA while Citi adds a 90-day downside catalyst watch to the stock. - Big bank stocks, including shares of Bank of America (BAC) slid after some analysts warned of downside risks. HSBC is turning cautious on three of the biggest US bank stocks following a record rally that's brought the group within shouting distance of an all-time high. 'Downside risks associated with still-elevated macro uncertainty, potentially slowing economic growth and more interest rate cuts through 2025 and 2026 are generally not factored into the stock prices,' analyst Saul Martinez wrote in a note downgrading JPMorgan Chase & Co., Goldman Sachs Group Inc. and Bank of America Corp. At the same time, 'the repricing of fixed-rate assets, benign credit quality, improving investment banking activity, and a favorable regulatory backdrop are well priced in.'
Bloomberg
an hour ago
- Bloomberg
UniCredit Boosts Its Equity Stake in Commerzbank to About 20%
UniCredit SpA, the Italian lender that hopes to acquire Commerzbank AG, increased its equity stake in the German firm and became its largest shareholder after converting derivatives into stock. The move gives Milan-based UniCredit about 20% of the effective voting rights and it intends to eventually convert its remaining 'synthetic position' to boost its Commerzbank holding to roughly 29%, according to a statement Tuesday. UniCredit announced the change after receiving approvals from the European Central Bank, German antitrust authorities and the US Federal Reserve.
