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.

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.

Overall market will grow, but there are still pockets of uncertainty involving memory, scaling and tariffs.

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

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

Improvements in power, performance and area are much more difficult to achieve, but solutions are coming into focus.

Shift to cloud model has kept power consumption in check, but that benefit may have run its course.

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

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.

The number of critical design metrics is expanding, but the industry still grapples with their implications.