There are multiple layers in which machine learning can help with the creation of semiconductors, but getting there is not as simple as for other application areas.

Technical issues increase, costs go up, and not all markets will benefit.

Machine learning needs techniques to prevent adversarial use, along with better data protection and management.

Funding is free-flowing for the field, but hurdles persist.

New lithography, equipment required as packaging continues to scale.

Chip industry repositions as technology begins to take shape; no clear winners yet.

Proponents tout freedom for computing architectures, but is the semiconductor ecosystem ready for open-source hardware?

There are multiple layers in which machine learning can help with the creation of semiconductors, but getting there is not as simple as for other application areas.

Part 2: Heterogeneity and architectures become focus as scaling benefits shrink; IP availability may be problematic.

Machine learning needs techniques to prevent adversarial use, along with better data protection and management.

Technology begins to twist in different directions and for different markets.

Adding more dimensions creates more data, all of which needs to be processed using new architectural approaches.

Experts at the table, part 1: What the toolchain looks like today and the different mindsets within those flows.