Using the Von Neumann architecture for artificial intelligence applications is inefficient. What will replace it?

DARPA and a number of major vendors are backing this modular approach, but hurdles remain.

D2S’ CEO sounds off on the impact of deep learning, EUV and other manufacturing advancements.

It’s been an uneven path leading to the current state of AI, and there’s still a lot of work ahead.

Assessing the reliability of a device requires adding more physical factors into the analysis, many of which are interconnected in complex ways.

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

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

What happens when companies are combined? The outcome often isn’t as good as the announcement.

Acceleration of advanced processes, skyrocketing complexity and cost, and concerns about IP availability are raising some difficult questions.

As complexity and device sizes rise, so does the need for an on-chip network.

Part 1: New opportunities and potential pitfalls in automotive, 5G, connected intelligence and infrastructure as a service in EDA.

Experts at the Table, part 1: While the Cloud may be ready for EDA, it is not clear how ready EDA is for the Cloud. What needs to change?

Synopsys Co-CEO Aart de Geus explains why systemic complexity will be the next big challenge, and what that means for EDA and the chip industry.