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

Shortages of used equipment and lower margins mean this problem isn’t getting solved anytime soon.

At future nodes there are some unexpected behaviors. What to do about them isn’t always clear.

What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.

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

This more than Moore technology is still ramping up, and problems need to be solved, but it could lead to some fundamental changes.

Cost of porting tools and IP will limit choices at partial nodes and create confusion at others.

The race is on to simulate thermal and electromagnetic effects.

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

The ways different architectures get around the memory bottleneck.

The next-gen wireless technology is riddled with problems, but that hasn’t slowed the pace of development.

Electric cars, mobile devices and industrial applications are focusing renewed attention on this technology.

Definitions vary by market and by vendor, but an explosion of data requires more processing to be done locally.