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

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

Experts at the Table, part 1: Why machine learning works in some cases and not in others.

Continued integration is no longer the natural way forward for semiconductors. What needs to happen to make it easier?

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

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.

But working with this architecture has some not-so-obvious pitfalls, and new tools licensing options may be necessary.

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.

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.