A combination of AMD's 3D V-Cache, AI, and multiple cores offers enthusiasts a bold new opportunity for tweaking the performance of their PCs. But a new Asus BIOS option, Asus AI Cache Boost, takes the potential complexity out of it all, offering double-digit performance increases just by enabling the Cache Boost option. We've already discovered that you can boost the performance of a Ryzen AI Max processor by up to 60 percent just by adjusting the UMA frame buffer. The new Asus BIOS option offers a related tweak specifically for AMD Ryzen 9950X3D and 9900X3D processors. Naturally, the performance varies depending upon the type of applications being run.

Intel's new Core Ultra 200 processors offer a huge leap forward in performance on top of all-day battery life. But these new "Arrow Lake" chips leave out an absolute necessity of today's PCs: an NPU, the engine which powers AI performance across the board. We knew this going into my review of Intel's Core Ultra 9 285H inside of an MSI laptop. But it might be time for Intel -- and maybe AMD, too -- to take a step back and consider what consumers really want: a "good," one-size-fits-all mainstream PC. And a clear way to identify them! Every time I review a chip or another product, I try to unearth the "story" behind it.

AMD is targeting both low-end and high-end AI PCs at CES 2025. The company unveiled a new family of Ryzen AI Max chips meant for "halo" Copilot AI PCs, which will sit above existing Ryzen AI 9 systems. Clearly, AMD wants AI PC options for everyone. To its credit, AMD's Ryzen AI Max chips seem like powerhouses. They feature up to 16 Zen 5 performance cores, 40 RDNA 3.5 GPU compute units and 50 TOPS of AI performance with AMD"s XDNA 2 NPU.

Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics and bioinformatics, inevitably becoming a significant category of workload on high performance computing (HPC) systems. Existing AI benchmarks tend to customize well-recognized AI applications, so as to evaluate the AI performance of HPC systems under predefined problem size, in terms of datasets and AI models. Due to lack of scalability on the problem size, static AI benchmarks might be under competent to help understand the performance trend of evolving AI applications on HPC systems, in particular, the scientific AI applications on large-scale systems. In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications. To enable scalability, SAIH builds a set of novel mechanisms for augmenting problem sizes. As the data and model constantly scale, we can investigate the trend and range of AI performance on HPC systems, and further diagnose system bottlenecks. To verify our methodology, we augment a cosmological AI application to evaluate a real HPC system equipped with GPUs as a case study of SAIH.

The arrival of artificial intelligence (AI) in embedded computing has led to a proliferation of potential solutions that aim to deliver the high performance required to perform neural-network inferencing on streaming video at high rates. Though many benchmarks such as the ImageNet challenge work at comparatively low resolutions and can therefore be handled by many embedded-AI solutions, real-world applications in retail, medicine, security, and industrial control call for the ability to handle video frames and images at resolutions up to 4kp60 and beyond. Scalability is vital and not always an option with system-on-chip (SoC) platforms that provide a fixed combination of host processor and neural accelerator. Though they often provide a means of evaluating the performance of different forms of neural network during prototyping, such all-in-one implementations lack the granularity and scalability that real-world systems often need. In this case, industrial-grade AI applications benefit from a more balanced architecture where a combination of heterogeneous processors (e.g., CPUs, GPUs) and accelerators cooperate in an integrated pipeline to not just perform inferencing on raw video frames but take advantage of pre- and post-processing to improve overall results or handle format conversion to be able to deal with multiple cameras and sensor types.

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.

Scott Bair is a Senior Technical Creative Director for Intel Labs, chartered with growing awareness for Intel's leading-edge research activities, like AI, Neuromorphic Computing and Quantum Computing. Scott is responsible for driving marketing strategy, messaging, and asset creation for Intel Labs and its joint-research activities. In addition to his work at Intel, he has a passion for audio technology and is an active father of 5 children. Scott has over 23 years of experience in the computing industry bringing new products and technology to market. During his 15 years at Intel, he has worked in a variety of roles from R&D, architecture, strategic planning, product marketing, and technology evangelism.

Did you miss a session at the Data Summit? After much speculation, Nvidia today at its March 2022 GTC event announced the Hopper GPU architecture, a line of graphics cards that the company says will accelerate the types of algorithms commonly used in data science. Named for Grace Hopper, the pioneering U.S. computer scientist, the new architecture succeeds Nvidia's Ampere architecture, which launched roughly two years ago. The first card in the Hopper lineup is the H100, containing 80 billion transistors and a component called the Transformer Engine that's designed to speed up specific categories of AI models. Another architectural highlight includes Nvidia's MIG technology, which allows an H100 to be partitioned into seven smaller, isolated instances to handle different types of jobs.