You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • 芯片发展史与AI芯片体系结构简介
    Summary: 课程情况介绍,简要介绍芯片发展历程,以及简要介绍前沿AI芯片体系结构
    [slides] [supplementary slides]

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  • 电路基础-1 晶体管与数字电路设计
    Summary: 晶体管原理及数字电路基础,为后续学习更复杂的电路结构做准备
    [slides] [supplemantary slides]

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  • 电路基础-2 时序电路、芯片的物理设计与验证
    Summary: 介绍时序电路、状态机以及芯片的设计流程,涵盖从前端、后端到最后流片的整体过程
    [slides] [supplementary slides]

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  • 指令集与流水线设计
    Summary: 介绍指令集ISA基础与流水线CPU的设计原理
    [slides]

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  • 数据/控制冲突以及处理机制
    Summary: 介绍流水线CPU中的数据/控制冲突与处理方法
    [slides]

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  • 指令动态发射原理
    Summary: 介绍乱序执行指令动态发射原理,以MIPS R10K为例
    [slides]

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  • 分支预测与超标量设计
    Summary: 介绍分支预测技术与超标量技术
    [slides]

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  • 多级缓存与缓存一致性
    Summary: 介绍多级缓存结构与Cache Coherence相关知识
    [slides]

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  • 缓存一致性与预读取
    Summary: 介绍缓存一致性、预读取、虚拟缓存与多线程等相关概念
    [slides]

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  • 多核多线程与智能优化
    Summary: 介绍GPU架构与编译层面优化
    [slides]

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  • 人工智能加速器架构I
    Summary: 介绍人工智能加速器架构
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  • 人工智能加速器架构II
    Summary: 介绍有代表性的前沿AI加速器架构与设计
    [slides]

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  • 软硬件协同设计
    Summary: 介绍前沿软硬件协同技术,如量化剪枝蒸馏等
    [slides]

    Suggested Readings

    • Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
    • ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
    • OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
    • EIE: Efficient inference engine on compressed deep neural network
    • SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference
    • NV Sparse TensorCore
  • 近存计算与存内计算
    Summary: 介绍近几年近存计算、存内计算技术特点与局限
    [slides]
  • 未来AI芯片发展趋势&Guest Lecture
    Summary: 总结学期课程,介绍未来AI芯片发展趋势,华为朱晓明老师Guest Lecture

    感谢朱晓明老师!