计算机架构的AlphaZero时刻：在被包围的世界中进行自动化发现
Computer Architecture's AlphaZero Moment: Automated Discovery in an Encircled World
AI总结：
计算机架构面临摩尔定律终结的挑战，传统方法已无法满足性能需求，本文提出通过自动化系统加速架构设计，显著提升效率。
摘要：
摩尔定律和 Dennard 缩放定律的终结已深刻改变了计算机架构的经济性。随着晶体管缩放带来 diminishing returns，架构创新已成为性能提升的主要甚至唯一杠杆。然而，我们认为人类驱动的架构研究在新时代本质上不适用。架构设计空间广阔（实际上无限），但人类团队每代可能只探索50-100种设计，样本不足0.001%。在资源匮乏的新时代，每个架构必须在相同晶体管预算下提供2倍性能提升，系统性探索变得至关重要。我们提出一个具体替代方案：自动化想法工厂，通过多级评估管道每周生成和评估数千种候选架构，通过连续反馈循环学习已部署的 telemetry 数据。初步结果表明，此类系统可将架构设计周期从双位数月压缩到单位数周，通过探索比任何人类团队多数量级的候选方案，并且速度更快。我们预测在两年内，纯粹由人类驱动的架构研究将变得像人类棋手与引擎对抗一样过时。
英文摘要：
The end of Moore's Law and Dennard scaling has fundamentally changed the economics of computer architecture. With transistor scaling delivering diminishing returns, architectural innovation is now the primary - and perhaps only - remaining lever for performance improvement. However, we argue that human-driven architecture research is fundamentally ill-suited for this new era. The architectural design space is vast (effectively infinite for practical purposes), yet human teams explore perhaps 50-100 designs per generation, sampling less than 0.001% of possibilities. This approach worked during the abundance era when Moore's Law provided a rising tide that lifted all designs. In the current scarcity paradigm, where every architecture must deliver 2X performance improvements using essentially the same transistor budget, systematic exploration becomes critical. We propose a concrete alternative: automated idea factories that generate and evaluate thousands of candidate architectures weekly through multi-tiered evaluation pipelines, learning from deployed telemetry data in a continuous feedback loop. Early results suggest that such systems can compress architectural design cycles from double-digit months to single-digit weeks by exploring orders of magnitude more candidates than any human team, and do it much faster. We predict that within 2 years, purely human-driven architecture research will be as obsolete as human chess players competing against engines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
