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MiniMax has open-sourced MCode Stack, an MIT-licensed repo of 17 SKILL.md skills and 5 checklists that its MiniMax Code team uses daily for code review, subagent splits, context budgets and long-running commands. The post MiniMax Open-Sources MCode Stack: 15 Agent Skills Its Coding Team Uses Daily appeared first on MarkTechPost.
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI. These powerful systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified […]
On October 28, it was reported that OpenAI plans to procure AI chips from Google. For a long time, OpenAI has been a major purchaser of NVIDIA GPUs, relying on them for training and inference of large-scale models. As computing power demands surge, companies are seeking more cost-effective alternatives suitable for running AI models. Google’s self-developed TPUs have gradually gained prominence in recent years, emerging as a potential option. This move may reflect a trend toward diversification in AI chip procurement.
Evaluate AI agents from workload specification through GDS layout, using open-source tools and an open-source PDK to assess their ability to complete the chip-design workflow. The post Introducing Build-A-Chip-Bench: From Workload Specification to GDS appeared first on Semiconductor Engineering.
Make every layer of your hardware PDN-aware and reach your maximum compute density. The post Foundations of Power Management in the Age of AI appeared first on Semiconductor Engineering.
The growing role of AI in analog and mixed-signal design, where complexity is rising, schedules are tightening, and the cost of mistakes is higher than ever. The post Using AI for Analog Design appeared first on Semiconductor Engineering.
How a single, integrated Foundation IP platform reduces re-spin risk and integration delays and helps lower TCO. The post Foundation IP For The AI Era: Why Execution Saves You Millions On HPC And Edge AI SoCs appeared first on Semiconductor Engineering.
Agentic solutions for RF design. The post Planning Your AI Design Journey In RF EDA appeared first on Semiconductor Engineering.
STCO for heterogeneous design; physical AI interactions; signing off AI-generated designs; workload profile analysis. The post Blog Review: Oct. 28 appeared first on Semiconductor Engineering.
Many AI workloads need only a fraction of a GPU. Giving each one an entire device can leave capacity idle; sharing GPUs lets platform teams serve more workloads...
Researchers at IBM Research Europe published “Superposed analog memory for vector storage in neural networks.” Summary: Researchers demonstrated an analog phase-change memory architecture that stores multiple AI vectors in superposition, increasing effective storage density with a tunable accuracy tradeoff. Tests on prototype PCM hardware showed inference accuracy close to full-precision software. Find the technical paper here.... » read more The post Analog Superposition Increases Effective PCM Storage Density (IBM Research Europe) appeared first on Semiconductor Engineering.
Seekable OCI (SOCI) indexing for Amazon SageMaker AI training and processing jobs takes full container image download off the critical path. SageMaker AI lazy-loads your image to start containers in seconds instead of minutes, with no changes to your job definition. See the benchmarks and how to adopt it.
NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX...
Simplismart says its annualized revenue run rate has grown 14-fold in six months, with the U.S. accounting for more than half its revenue.
On October 27, Rokid launched the AI glasses Rokid Glasses 2, the full-color AI glasses Rokid Color, and the interactive ring Rokid Ring.
Index implementation plus ETF authorization have provided Hong Kong‑listed AI and semiconductor stocks with new allocation tools and a fresh source of capital.
Unprecedented demand for bandwidth calls for bringing optics closer to silicon through a fundamental rethinking of the optical interconnect roadmap. The post The imec Report: Scaling Optical Interconnects For The AI Era appeared first on Semiconductor Engineering.
arXiv:2610.34907v1 Announce Type: new Abstract: The Tensor Memory Accelerator (TMA), introduced on NVIDIA Hopper, is used pervasively in production GPU kernels yet its usage is conspicuously selective: expert kernel developers in CUTLASS, FlashAttention-3, and FlashInfer apply TMA to some tensor operands within a kernel but fall back to cp.async, direct global loads, or distributed shared memory (DSM) for others. Vendor documentation describes the API, and recent microbenchmarking work characterizes TMA throughput in isolation, but neither answers the question practitioners actually face: given a memory operand in a kernel, should it use TMA, and if so, with or without cluster multicast? We address this gap with a workload-grounded characterization study on H100. We catalog the TMA, multic
arXiv:2610.33275v1 Announce Type: new Abstract: Computational notebooks reserve a GPU for the whole session, yet the GPU computes only while a cell executes and sits idle otherwise. Serverless execution would release the GPU between cells, but reconstructing the accumulated notebook state on a fresh worker imposes substantial cost on every GPU cell. This paper presents LooseLeaf, a serverless GPU platform for interactive notebooks. LooseLeaf derives the state and import closures of each cell, the minimal variables and packages that the cell needs. Our analysis on 5,000 real-world notebooks shows that these closures are small, and user-cell editing leaves seconds to prepare them. LooseLeaf prepares a GPU worker with these closures while the user edits the cell. After execution, the worker s
arXiv:2610.33475v1 Announce Type: new Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward distributed, memory-intensive generative workloads. However, serving massive foundation models under stringent Service Level Objectives (SLO) exposes critical physical and system bottlenecks. These challenges include the persistent High Bandwidth Memory (HBM) wall, operational asymmetry between compute-bound prefilling and memory-bandwidth-bound decoding, explosive Key-Value (KV) cache expansion, prohibitive serverless cold starts, and hardware reliability vulnerabilities. Existing literature reviews remain layer-siloed, decoupling low-level accelerator virtualization from cluste
