AgenticCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agentic Evolution

AgenticCANN addresses the problem of automated Ascend C operator generation for NPUs, which requires deep hardware expertise and faces a severe platform knowledge deficit. The method introduces a knowledge-augmented agentic evolution framework with a knowledge-orchestrated generation system and a stage-adaptive agentic evolution strategy to dynamically align LLM interaction modes. Experiments on Huawei Ascend 910B across six operators show 90-100% feasibility on elementwise and normalization operators, 56% on fusion operators, and up to 6.65× speedup on 1B Pangu model inference kernels, with knowledge injection monotonically improving feasibility from 57% to 86%. This matters because it enables automated, high-performance operator synthesis in low-corpus NPU environments, overcoming the unique challenges of the Ascend C programming model.