公众号记得加星标⭐️，第一时间看推送不会错过。 随着工艺技术的进步，性能和晶体管密度提升的潜力日益受到功耗和散热限制。尽管材料、互连和器 件结构的创新仍然至关重要，但它们现在必须与架构策略紧密结合，才能充分实现系统级效率。与此 同时，人工智能计算需求的爆炸式增长已经超越了传统的扩展曲线，这加剧了架构和工艺技术在严格 的功耗和散热限制下实现前所未有的性能的压力。 本文重点阐述了微架构和工艺技术的协同设计如何应对不断增长的热密度、功耗挑战和性能需求，并 敦促工艺研究人员在其扩展路线图中考虑架构的影响。 引言 摩尔定律并未失效，但它正在经历深刻的变革。在原子级材料工程、导电金属层、三维晶体管层、背 面供电、新型高密度三维封装等诸多领域的新研究推动下，晶体管尺寸不断缩小，但传统的尺寸缩小 优势正日益受到功率密度和散热限制的挑战。随着晶体管尺寸的缩小和三维结构的普及，集成度不断 提高，性能瓶颈也随之转移：如今的系统不再受限于晶体管的开关速度或数量，而是越来越依赖于其 有效管理能量和散热的能力。 与此同时，人工智能工作负载的爆炸式增长——其特点是海量模型、密集型训练流程和低延迟推理 ——使计算需求呈数量级增长，进一步加剧 ...

Core Insights - The article emphasizes the rapid evolution of GPU computing capabilities in artificial intelligence and deep learning, driven by Tensor Core technology, which significantly outpaces Moore's Law [1][3] - It highlights the importance of understanding the architecture and programming models of Nvidia's GPUs to grasp the advancements in Tensor Core technology [3] Group 1: Performance Principles - Amdahl's Law defines the maximum speedup achievable through parallelization, emphasizing that performance gains are limited by the serial portion of a task [5] - Strong and weak scaling are discussed, where strong scaling refers to improving performance on a fixed problem size, while weak scaling addresses solving larger problems in constant time [6][8] Group 2: Data Movement and Efficiency - Data movement is identified as a significant performance bottleneck, with the cost of moving data being much higher than computation, leading to the concept of the "memory wall" [10] - Efficient data handling is crucial for maximizing GPU performance, particularly in the context of Tensor Core operations [10] Group 3: Tensor Core Architecture Evolution - The article outlines the evolution of Nvidia's Tensor Core architecture, including Tesla V100, A100, H100, and Blackwell GPUs, detailing the enhancements in each generation [11] - The introduction of specialized instructions like HMMA for half-precision matrix multiplication is highlighted as a key development in Tensor Core technology [18][19] Group 4: Tensor Core Generations - The first generation of Tensor Core in the Volta architecture supports FP16 input and FP32 accumulation, optimizing for mixed-precision training [22][27] - The Turing architecture introduced the second generation of Tensor Core with support for INT8 and INT4 precision, enhancing capabilities for deep learning applications [27] - The Ampere architecture further improved performance with asynchronous data copying and introduced new MMA instructions that reduce register pressure [29][30] - The Hopper architecture introduced Warpgroup-level MMA, allowing for more flexible and efficient operations [39] Group 5: Memory and Data Management - The introduction of Tensor Memory (TMEM) in the Blackwell architecture aims to alleviate register pressure and improve data access efficiency [43] - The article discusses the importance of structured sparsity in enhancing Tensor Core throughput, particularly in the context of the Ampere and Hopper architectures [54][57] Group 6: Performance Metrics - The article provides comparative metrics for Tensor Core performance across different architectures, showing significant improvements in FLOP/cycle and memory bandwidth [59]