AI Native: What Does It Mean For Embedded Processing?


Artificial intelligence (AI) continues transforming how users interact with technology. AI-powered chatbots like ChatGPT, along with advancements in data analytics and mobile technology, have contributed to high expectations among tech users. Consumers want and expect faster, more personalized, and smarter experiences — whether they're controlling a smart home device or speaking to a chatbot ... » read more

Embarrassingly Parallel Problems: Definitions, Challenges And Solutions


One of the reasons GPUs are regularly discussed in the same breath as AI is that AI shares the same fundamental class of problems as 3D graphics. They are both embarrassingly parallel. Embarrassingly parallel problems refer to computational tasks that: Exhibit independence: Subtasks do not rely on intermediate results from other tasks. Require minimal interaction: Parallel task... » read more

Photonics Speeds Up Data Center AI


Photonics is playing an increasingly vital role in the acceleration of AI within data centers. The global market for optical components is already substantial, accounting for $17 billion in revenue last year. Historically, telecommunications — such as undersea cables and fiber-to-the-home — dominated demand. However, the datacom sector, especially AI-driven data centers, now accounts for... » read more

Smarter Cars, Higher Stakes


Artificial intelligence is turbocharging automotive innovation, but it's also unleashing a tangle of high stakes risks that engineers and security experts are scrambling to contain. The push to embed AI deep into today’s vehicles is changing how cars are built, how they handle the road, and how they keep passengers safe. But as onboard intelligence expands, so do the risks. AI systems that... » read more

From Tool Agents To Flow Agents


Experts At The Table: AI is starting to impact several parts of the EDA design and verification flows, but so far these improvements are isolated to single tool or small flows provided by a single company. What is required is a digital twin of the development process itself on which AI can operate. Semiconductor Engineering sat down with a panel of experts to discuss these issues and others, in... » read more

Tape-Out Failures Are The Tip Of The Iceberg


The headline numbers for the new Wilson Research/Siemens functional verification survey are out, and it shows a dramatic decline in the number of designs that are functionally correct and manufacturable. In the past year, that has dropped from 24% to just 14%. Along with that, there is a dramatic increase in the number of designs that are behind schedule, increasing from 67% to 75%. Over the ne... » read more

AI Drives Re-Engineering Of Nearly Everything In Chips


AI's ability to mine patterns across massive quantities of data is causing fundamental changes in how chips are used, how they are designed, and how they are packaged and built. These shifts are especially apparent in high-performance AI architectures being used inside of large data centers, where chiplets are being deployed to process, move, and store massive amounts of data. But they also ... » read more

AI-Driven Verification Regression Management


By Paul Carzola and Taruna Reddy Coping with the endless growth in chip size and complexity requires innovative electronic design automation (EDA) solutions at every stage of the development process. Better algorithms, increased parallelism, higher levels of abstraction, execution on graphics processing units (GPUs), and use of AI and machine learning (ML) all contribute to these solutions. ... » read more

New Ways To Improve EDA Productivity


EDA vendors are taking aim at new ways to improve the productivity of design and verification engineers, who are struggling to keep pace with exponential increases in chip complexity in tight time-to-market windows and with constrained engineering talent pipelines. In the past, progress often was as straightforward as improving algorithms or parallelizing computations in a linear flow. But w... » read more

Need For Speed Drives Targeted Testing


As packaging complexity increases and nodes shrink, defect detection becomes significantly more difficult. Engineers must contend with subtle variations introduced during fabrication and assembly without sacrificing throughput. New material stacks degrade signal-to-noise ratios, which makes metrology more difficult. At the same time, inspection systems face a more nuanced challenge — how t... » read more

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