AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization

• URL: http://arxiv.org/abs/2603.23566v1
• Date: Tue, 24 Mar 2026 08:54:53 GMT
• Title: AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
• Abstract summary: We present Ascendr, an episodic agent that bootstraps this missing expertise by turning execution into experience.<n>Ascendr achieves a 1.19x geometric-mean speedup over the open-source baseline, with 49.61% of operators their references, outperforming strong agent and search baselines.
• Score: 13.951709632040908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AscendC (Ascend C) 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 - a host-side tiling program that orchestrates data movement and a kernel program that schedules and pipelines instructions. We present AscendOptimizer, an episodic agent that bootstraps this missing expertise by turning execution into experience. On the host side, AscendOptimizer performs profiling-in-the-loop evolutionary search to discover valid and high-performing tiling and data-movement configurations directly from hardware feedback. On the kernel side, it mines transferable optimization motifs by rewinding optimized kernels - systematically de-optimizing them to synthesize instructive "bad-to-good" trajectories - and distills these motifs into a retrievable experience bank for guided rewriting. By alternating host tuning and kernel rewriting in a closed loop, AscendOptimizer steadily expands feasibility and pushes latency down. On a benchmark of 127 real AscendC operators, AscendOptimizer achieves a 1.19x geometric-mean speedup over the open-source baseline, with 49.61% of operators outperforming their references, outperforming strong agent and search baselines.

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