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?

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.

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

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

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

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

Edge compute covers a wide range of applications. Understanding bandwidth and capacity needs is critical.

In-chip monitoring techniques are growing for automotive, industrial, and data center applications.

Different types and approaches can have a big impact on cost, power, bandwidth and latency.

Capacity, speed, power and cost become critical factors in memory for AI/ML applications.