Blog Review: Aug. 16


Synopsys' Johannes Stahl and Tim Kogel suggest that multi-die systems require a new approach at the architecture planning phase and why chip designers can’t ignore physical effects such as layout, power, temperature, or IR-drop. Siemens' Rich Edelman argues for using the waveform window in a GUI rather than $display when debugging UVM. Cadence's Paul Scannell stresses the need for diver... » read more

Why It’s So Difficult To Ensure System Safety Over Time


Safety is emerging as a concern across an increasing number of industries, but standards and methodologies are not in place to ensure electronic systems attain a defined level of safety over time. Much of this falls on the shoulders of the chip industry, which provides the underlying technology, and it raises questions about what more can be done to improve safety. A crude taxonomy recently ... » read more

Processor Tradeoffs For AI Workloads


AI is forcing fundamental shifts in chips used in data centers and in the tools used to design them, but it also is creating gaps between the speed at which that technology advances and the demands from customers. These shifts started gradually, but they have accelerated and multiplied over the past year with the rollout of ChatGPT and other large language models. There is suddenly much more... » read more

MRAM Getting More Attention At Smallest Nodes


Magneto-resistive RAM (MRAM) appears to be gaining traction at the most advanced nodes, in part because of recent improvements in the memory itself and in part because new markets require solutions for which MRAM may be uniquely qualified. There are still plenty of skeptics when it comes to MRAM, and lots of potential competitors. That has limited MRAM to a niche role over the past couple de... » read more

Cleaning Marine Geometries Has Never Been Easier


Ship designers and naval architects increasingly use computational fluid dynamics (CFD) tools for more accurate solutions, detailed physics, and quicker results. Marine ship design studies in the past relied mainly on scaled-down models in towing tanks for insights into ship resistance, seakeeping, propulsion, and maneuvering. However, these models had discrepancies in their Reynolds and Fro... » read more

Blog Review: Aug. 9


Synopsys' John Swanson and Manmeet Walia note that designing for 224G Ethernet will entail some unique considerations, as design margins will be extremely tight, making it mission-critical to optimize individual analog blocks to reduce impairments. Cadence's Rick Sanborn finds that knowing how best to debug common partitioning-related issues and implicitly control them using common features ... » read more

Chiplets: Deep Dive Into Designing, Manufacturing, And Testing


Chiplets are a disruptive technology. They change the way chips are designed, manufactured, tested, packaged, as well as the underlying business relationships and fundamentals. But they also open the door to vast new opportunities for existing chipmakers and startups to create highly customized components and systems for specific use cases and market segments. This LEGO-like approach sounds ... » read more

Week In Review: Auto, Security, Pervasive Computing


Hyundai, Samsung Catalyst Fund, and others invested a combined $100 million in Canada-based Tenstorrent to accelerate the design and development of AI chiplets and machine-learning software and allow the integration of AI into future Hyundai, Kia, and Genesis vehicles, plus other future mobilities such as robotics and advanced air mobility (AAM). The National Highway Traffic Safety Administr... » read more

Week In Review: Design, Low Power


Qualcomm, NXP, Infineon, Nordic, and Bosch are jointly investing in a new RISC-V company, to be formed in Germany, that will speed up RISC-V’s adoption in commercial products. The company will be “a single source to enable compatible RISC-V based products, provide reference architectures, and help establish solutions widely used in the industry,” according to a press release. The co... » read more

AI Transformer Models Enable Machine Vision Object Detection


The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the models and simplify their development. Over the years, many AI models have been introduced, including YOLO, Faster R-CNN, Mask R-CNN, RetinaNet, and others, to detect images or video signals, interp... » read more

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