AI Memory Architecture Race

Analyze the key tradeoffs in designing memory systems for large-scale AI accelerators. Compare high-bandwidth memory, memory stacking, interconnect topologies, and energy efficiency. Propose a framework to evaluate how these choices impact performance, cost, and deployment in data centers across industries, and outline a research roadmap for evolving memory standards to support future AI workloads.