Blog Review: Oct. 1


Synopsys' Chun-Soo Kim and Hoseong Kim suggest making the entire design flow local layout effect-aware to identify and address issues early and ultimately improve PPA by avoiding overly pessimistic designs. Siemens' Kirk Fabbri explores the power distribution network, focusing on power plane capacitance and how it varies with the dynamic switching characteristics of the load and dielectric c... » read more

Blog Review: Sept. 24


Siemens' Harry Foster warns of a big drop in first-time silicon success as more system companies tackle developing their own chip without the accumulated knowledge around flows, sign-off criteria, and coverage closure in a landscape where even small oversights in methodology can lead to multimillion-dollar respins. Synopsys' Godwin Maben warns that skyrocketing power consumption is a critica... » read more

Blog Review: Sept. 17


Siemens' John McMillan explores the fundamentals of IC package thermal resistance, modeling strategies, and why die-level thermal analysis in 3D-ICs is increasingly essential for ensuring device reliability. Cadence's Jasmine Makhija provides an overview of the TEE Device Interface Security Protocol (TDISP), which helps safeguard PCIe devices within Trusted Execution Environments by providin... » read more

Chip Industry Week in Review


The U.S. is considering annual approvals for Samsung and SK hynix to export chipmaking tools and materials to their factories in China, replacing perpetual waivers granted under the validated end user system, reports Bloomberg. The proposal, presented by the U.S. Commerce Department to South Korean officials, would require the companies to reapply each year for specific quantities of restricted... » read more

Balancing Workloads In AI Processor Designs


A growing number of AI processors are being designed around specific workloads rather than standardized benchmarks, optimizing performance and power efficiency, but often with enough flexibility to adapt to future changes. While the fundamentals of matrix multiplication and software optimization still apply, those alone are no longer sufficient. Designs need to address specific data types, w... » read more

How Neural Super Sampling Works: Architecture, Training, And Inference


This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we announced Arm neural technology that will ship in Arm GPUs in 2026. The first use case of the technology is Neural Super Sampling (NSS). NSS is a next-generation, AI-powered upscaling solution. ... » read more

Report: The Road to Artificial General Intelligence: Achieving the Next Era of Intelligence


Explore how industry leaders are defining artificial general intelligence (AGI) and what it may take to reach it. Developed by MIT Technology Review and Arm, this deep dive examines accelerating timelines, the compute innovations shaping progress, and why today’s models still fall short of true intelligence. Designed for engineers, researchers, and technology leaders navigating the future of ... » read more

Blog Review: Sept. 10


Cadence's Satish Kumar C explains Port-Based Routing, a feature in in CXL 3.0 and 3.1 that changes how CXL switches operate within a CXL fabric to enable the creation of much larger, more flexible, and more efficient topologies. Siemens' Bill Hargin demystifies copper foil thickness and weight measurements and why being precise has an impact on signal integrity and crosstalk simulations.... » read more

Cloud vs. Edge Gaming: Performance Gap Is Shrinking


Chip designers and gaming companies are scrambling to figure out whether the gaming market will tilt toward the cloud, the edge, or some combination of both. Multi-gigabit internet allows more people to play high-end games in the cloud, but edge-based gaming consoles and devices remain well-rooted, more secure, and private. Which one wins? So far, there are more questions than answers. Handh... » read more

Blog Review: September 3


Cadence's Sriram Sharma Kalluri compares convolutional neural networks (CNNs) and transformers to show how their different architectures give them particular strengths and why the choice between them depends on the specific task, the available data, and the computational resources. Siemens' John McMillan provides a primer on the major IC package types, how they influence system design, therm... » read more

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