文献阅读《Chakra: Advancing performance benchmarking and co-design using standardized execution traces》

1. 文章简介 1.1 摘要 基准测试和协同设计对于推动 ML 模型、ML 软件和下一代硬件的优化和创新至关重要。全工作量基准(如 MLPerf)在实现不同软件和硬件堆栈之间的公平比较方面发挥着至关重要的作用,尤其是在系统完全设计和部署之后。然而,人工智能创新的步伐要求模拟器和仿真器采用更加敏捷的方
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