Optimizing Scan Test For Complex ICs


As chips become more heterogeneous with more integrated functionality, testing them presents increasing challenges — particularly for high-speed system-on-chip (SoC) designs with limited test pin availability. In addition, the complexity of emerging packages like 3D and chiplets necessitates comprehensive new solutions that can provide faster results at multiple stages in the silicon lifec... » read more

The Data Revolution Of Semiconductor Production


During our insightful panel discussion on “The Data Revolution of Semiconductor Production – How Advancements in Technology Unlock New Insights,” we covered several topics including machine learning, edge computing and cloud-based data management. We discussed questions including: Are we creating the right data and doing enough with it? What needs to be done to make data actionable? Ho... » read more

ML Automotive Chip Design Takes Off


Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications. On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine ... » read more

Role Of IoT Software Expanding


IoT software is becoming much more sophisticated and complex as vendors seek to optimize it for specific applications, and far more essential for vendors looking to deliver devices on-time and on-budget across multiple market segments. That complexity varies widely across the IoT. For example, the sensor monitoring for a simple sprinkler system is far different than the preventive maintenanc... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Accelerating Coverage Closure With AI-Based Verification Space Optimization


Coverage is at the heart of all modern semiconductor verification. There is no maxim more fundamental to this process than “if you haven’t exercised it, you haven’t verified it.” Although covering a particular aspect of a chip design does not guarantee that all bugs are found — bug effect propagation and checker quality are also key factors — it is certainly true that bugs cannot po... » read more

From Data Center To End Device: AI/ML Inference With GDDR6


Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inference. As inference migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be incre... » read more

AI Adoption Slow For Design Tools


A lot of excitement, and a fair amount of hype, surrounds what artificial intelligence (AI) can do for the EDA industry. But many challenges must be overcome before AI can start designing, verifying, and implementing chips for us. Should AI replace the algorithms in use today, or does it have a different role to play? At the end of the day, AI is a technique that has strengths and weaknesses... » read more

Can AI Write RTL?


Just a few months ago, generative AI was just a promise about what would be possible in the future. Today, nearly everyone with an ounce of curiosity has tried ChatGPT. Most people appear to be somewhat impressed with what it can do, but at the same time see the limitations that it has. As Dean Drako, founder of several companies, told me: "Recently, I needed to write a patent. I described t... » read more

EDA Makes A Frenzied Push Into Machine Learning


Machine learning is becoming a competitive prerequisite for the EDA industry. Big chipmakers are endorsing and demanding it, and most EDA companies are deploying it for one or more steps in the design flow, with plans to add much more over time. In recent weeks, the three largest EDA vendors have made sweeping announcements about incorporating ML into their tools at their respective user eve... » read more

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