Tech Talk: The statistical underpinnings of safety standards.
Jorg Gosse, functional safety product manager at OneSpin Solutions, talks about the statistics behind the standards, what is considered good enough, and how those numbers vary across different standards.
A primary goal of machine learning is to use machines to train other machines. But what happens if there’s malware or other flaws in the training data?
Momentum growing for low-cost alternatives to interposers as a way of reducing overall development costs.
What happens when companies are combined? The outcome often isn’t as good as the announcement.
This technology has resurfaced as one option at 5nm, but it’s still not clear if it will work.
D2S’ CEO sounds off on the impact of deep learning, EUV and other manufacturing advancements.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.
So far there are no tools and no clear methodology to eliminating bugs. That would require understanding what an AI bug actually is.
What happens when companies are combined? The outcome often isn’t as good as the announcement.
Acceleration of advanced processes, skyrocketing complexity and cost, and concerns about IP availability are raising some difficult questions.
Part 1: New opportunities and potential pitfalls in automotive, 5G, connected intelligence and infrastructure as a service in EDA.
As complexity and device sizes rise, so does the need for an on-chip network.
Experts at the Table, part 1: While the Cloud may be ready for EDA, it is not clear how ready EDA is for the Cloud. What needs to change?
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