Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.

Technology has the potential to reshape processing everywhere, starting with limited scientific and commercial applications.

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.

Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.

Goal is to improve quality while reducing time to revenue, but it’s not always so clear-cut.

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.

When does a large amount of data become Big Data, and could system-level verification benefit from it?

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.