In the cloud, AI runs in a kind of computational luxury. Thousands of GPUs and CPUs sit in climate-controlled buildings with access to ample power and memory. Utilization may be inefficient—often just ...

As data rates continue to increase, maintaining reliable links requires careful coordination between the PHY and controller ...

Why latency guarantees, memory movement, power budgets, and rapid model deployment now matter more than raw TOPS.

DRAM layout secrecy contributes to the problem, but there’s no indication that it will change. “We argue that keeping internal DRAM topologies secret hurts DRAM customers in several ways,” wrote ...

Power delivery now spans stacked dies, interposers, bridges, and packages connected by thousands of micro-bumps and TSVs.

Processor architectures are evolving faster than ever, but they still lag the pace of AI development. Chip architects must ...

As AI and high‑performance computing systems continue to scale, memory bandwidth has emerged as a primary system‑level ...

A complete pipeline that can run on a single workstation to train a humanoid robot to walk over rough terrain.

Validating an optimized data movement architecture that ensures arithmetic units receive a steady stream of data every cycle.

The number and variety of test interfaces, coupled with increased packaging complexity, are adding a slew of new challenges.

Limitations—such as latency, bandwidth costs, privacy concerns, catastrophic consequences in the event of failure, and ...

How next‑gen AI accelerators break past single‑chip limits using advanced IP, high‑speed interconnects, memory interfaces, ...