Problem
Research questions and friction points this paper is trying to address.
AI Datacenter
Hardware Architecture
Generality and Specialization
Workload Diversity
Data Movement
Balancing Generality and Specialization: A Survey on AI Datacenter Hardware Architecture
🤖 AI Summary
该论文通过分类和比较AI加速器架构，探讨了如何在通用性和专业化之间取得平衡以应对AI数据中心面临的挑战。
📝 Abstract
Rapidly growing AI workloads are driving large investments in AI datacenters. This survey classifies industrial AI accelerators into four architectural categories and compares their compute and memory organizations. It examines how node-, rack-, and pod-scale interconnects support collective communication, and traces architectural evolution across accelerator generations. The analysis connects advances in arithmetic throughput with changes in precision, data delivery, execution coordination, communication, power delivery, and cooling. It also discusses future design challenges arising from workload diversity, data movement, infrastructure constraints, and model evolution, showing how the trade-off between generality and specialization extends from individual accelerators to datacenter-scale systems. GitHub: github.com/Yufeng98/AI-datacenter
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI Accelerators
Datacenter Hardware Architecture
Interconnects
Architectural Evolution
Workload Diversity
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