Ed Sperling

Key Takeaways: HBM scaling is running into physical limitations. More bandwidth translates into higher area and capacity costs. AI demand is intensifying the DRAM shortage. DRAM is under stress, and the fallout is affecting the entire chip industry. Pressed by large systems companies to move data in and out of memory faster, the big three DRAM vendors — Micron, Samsung, and SK hynix — are wrestling with some increasingly complex tradeoffs.

Home > Low Power-High Performance > Redefining Roles For Edge And Cloud AI The race is on for localized intelligence. The rapid build-out of AI on the edge marks a fundamental shift in where intelligence is located, how much is included in devices, and the speed at which that intelligence can be applied to everyday problems. While the cloud will still be used for training large language models, the edge will include a collection of smaller, much more targeted language models.

Key Takeaways: Orders of magnitude performance improvements required for AI cannot happen without multi-die assemblies, new packaging options, and the underlying technologies and methodologies to enable them. Research initiated by IEEE’s International Roadmap for Devices and Systems is starting to gel across different market and technology segments. Massive compute power will be used to optimize these systems for performance, power, and cost.