The article discusses the evolving role of GPUs in AI computation as the technology expands beyond traditional data centers. It highlights the increasing relevance of FPGAs in the AI data path due to system constraints.
Microsoft has decided to extend support for Windows 10 for an additional year in response to user backlash. However, the effectiveness of this extension remains questionable.
Jensen Huang, cofounder and CEO of Nvidia, has been awarded the 2026 IEEE Medal of Honor for his contributions to the development of graphics processing units (GPUs) and their applications in scientific computing and artificial intelligence. The announcement was made by IEEE's president on January 6, 2026.
The article discusses enhancements in attention computation efficiency through the use of FP4 Tensor Cores in Blackwell GPUs, achieving significant speedups in inference. It also introduces an innovative 8-bit attention mechanism for training tasks, demonstrating lossless performance.
Q4 PC shipments increased by 9.6% year-over-year, totaling 76.4 million units, as reported by IDC. This growth was largely attributed to the end of support for Windows 10, prompting consumers to upgrade their devices.
The article discusses the concept of disaggregated Large Language Model (LLM) serving, which separates the prefill and decode stages across different GPUs. It highlights the need for systematic benchmarking of performance and energy efficiency in this approach, particularly concerning KV cache transfer methods and optimization strategies.
The article discusses the potential of stacking high-bandwidth memory (HBM) directly on top of GPUs to enhance performance by reducing the energy and delay associated with data transfer. This innovation could significantly alleviate the bottleneck currently faced in AI computing.
The article discusses Memory DisOrder, a timerless side-channel that exploits memory re-orderings to infer activity from other processes in parallel processing units. It highlights the vulnerability of mainstream processors to cross-process signals and demonstrates the potential for covert channel attacks and application fingerprinting.
The article introduces Cohet, a novel CXL-driven coherent heterogeneous computing framework that aims to enhance the interaction between CPUs and XPUs. It addresses the limitations of traditional heterogeneous systems by creating a unified memory pool and decoupling compute and memory resources.
Nvidia has unveiled its new Vera Rubin architecture at CES 2026, which promises significant reductions in inference costs and GPU requirements for training models compared to the previous Blackwell architecture. The platform is expected to be available to customers later this year.
The Consumer Technology Association (CTA) has announced the winners of the CES Innovation Awards for 2026, highlighting notable products from LG, Hisense, Yingling, and GeForce RTX. This recognition showcases advancements in consumer technology at the CES event.
The article discusses the critical role of capacitors in maintaining power integrity and reliability for NVIDIA H200 GPUs used in AI servers. It highlights the challenges posed by extreme transients, heat density, and power distribution network (PDN) stability.
The article introduces GAP-LA, a GPU-accelerated framework for optimizing layer assignment in VLSI circuit routing. This framework aims to improve timing, power, and congestion management simultaneously, addressing challenges posed by increasing design complexity.
The article highlights the top five PC vendors based on preliminary results for Q3 2025, as reported by IDC. It provides insights into worldwide traditional PC shipments, market share, and year-over-year growth.
The article discusses the limitations of current deep learning technologies that rely heavily on GPUs, which are expensive and energy-intensive. It introduces Neuro-Channel Networks (NCN), a new architecture inspired by biological signal transmission that eliminates the need for matrix multiplications.
The article introduces Warp Cortex, an innovative architecture designed to enhance cognitive scaling for multi-agent Large Language Models (LLMs) on consumer hardware. By optimizing memory usage and agent logic, it allows for significantly more concurrent agents than traditional frameworks.
The article introduces BitDecoding, a novel inference system that optimally utilizes both CUDA and Tensor Cores for decoding low-bit Key-Value caches in long-context Large Language Models. This approach aims to alleviate memory and bandwidth pressure while maintaining accuracy through advanced quantization techniques.
The article highlights that over half of the upcoming global data centers will be developed in the United States, indicating a significant concentration of data center activity. This trend underscores the U.S. dominance in the data center market, driven by land purchases and construction plans.
The article discusses the inefficiencies of current AI models compared to the human brain, particularly highlighting the energy consumption of artificial neural networks (ANNs) versus spiking neural networks (SNNs). It proposes a new framework for AI architecture that could significantly improve efficiency by leveraging principles from SNNs.
The article introduces HERO Sign, a GPU-accelerated implementation of the SPHINCS+ signature scheme that aims to enhance signature generation speed through hierarchical tuning and compiler optimizations. It highlights the potential for improved parallelism in the signature generation process, particularly through a new Tree Fusion strategy.