AscendOptimizer: An Episodic Agent for Ascend NPU Operator Optimization

Forum topic · 小凯 · 2026-03-27

Summary

AscendOptimizer is a research paper (arXiv:2603.23566) addressing operator optimization for Huawei Ascend neural processing units (NPUs). AscendC operator optimization faces a two-fold knowledge bottleneck: unlike the mature CUDA ecosystem, there are few public reference implementations to learn from, and performance depends on a coupled two-part artifact. The authors—Jiehao Wu, Zixiao Huang, Wenhao Li, Chuyun Shen, Junjie Sheng, and Xiangfeng Wang—propose AscendOptimizer, an episodic agent that bootstraps this missing expertise by converting execution into experience. The approach aims to enable automated, learning-driven optimization of AscendC operators, reducing reliance on scarce human expertise and reference code. Published on arXiv in the machine learning category, this work is relevant to AI compiler engineering, NPU software ecosystems, and agentic optimization methods.

Paper Overview

Research Area: Machine Learning (ML) Authors: Jiehao Wu, Zixiao Huang, Wenhao Li, Chuyun Shen, Junjie Sheng, Xiangfeng Wang Published: 2026-03-26 arXiv: 2603.23566

Background

AscendC operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck:

1. Unlike the CUDA ecosystem, there are few public reference implementations to learn from. 2. Performance hinges on a coupled two-part artifact.

Proposed Method

The authors present AscendOptimizer, an episodic agent that bootstraps this missing expertise by turning execution into experience. The system is designed to overcome the scarcity of reference material in the Ascend ecosystem by learning from optimization episodes.

Original Abstract (excerpt)

> AscendC operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck: unlike the CUDA ecosystem, there are few public reference implementations to learn from, and performance hinges on a coupled two-part artifact. We present AscendOptimizer, an episodic agent that bootstraps this missing expertise by turning execution into experience.

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