AKG PLDI 2021

AKG Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations

Posted by Treaseven on December 5, 2024

Challenges

之前设计的深度学习编译器主要目标的硬件平台是CPU、GPU、FPGA,没有考虑NPU,作者在针对这一硬件平台设计相应的编译器,有如下挑战: 1.在各种各样的计算单元的并行和空间/时间局部性的冲突需求 2.高效管理分级内存 3.建模在通用处理器架构没有出现过的优化方案

Overview of AKG

Polyhedral Transformations

  • versatile polyhedral scheduling
  • tiling(分块形状和分块大小)
  • fusion
  • storage management
  • optimization of convolution
  • manual scheduling and debugging

Code Generation

  • vectorization
  • low-level optimization of synchronization
  • auto tuning strategies

Evaluation

Reference

AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations