CANNBot-CAKE: Collaborative Agentic Kernel Evolution for Ascend NPU
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Efficient custom operators are essential for modern deep learning workloads, yet developing high-performance kernels for Ascend NPUs remains slow, expert-intensive, and difficult to automate. Although AI agents now approach expert performance on GPU kernel benchmarks, current models remain less capable of generating AscendC kernels for NPUs, potentially reflecting the relative scarcity of relevant training data. We thus present CAKE, a CANN-native system for deployable custom operators that combines adaptive multi-DSL routing, separate Generation and Evolution modes, and reviewed knowledge reuse through CAKE Book and a Self-Evolving Agent. On 53 CANN-Bench v1.1 operators on Ascend 950PR, CAKE's aggregated results achieve 100\% case pass rate on the standard set (1,060/1,060) and 98.28\% on the hidden set (4,167/4,240), with an overall mean score of 78.63 out of 100 and an average performance speedup of 1.334over the baseline. Evolution then pushes correct operators further: EngramGateFusion cuts aggregate device time by 46.6\% over 20 rounds and roughly halves the mean roofline gap. These results offer a practical path toward closing Ascend's AI-for-kernel capability gap.
Autores
Deng Pan, Xue-Jian Gao, Junyi Fan, Bin Du, Jiasheng Li, Xinxian Chen, Nok Yan Chang, Xinlin Li y 3 más
Categorías
overfeed.news indexa y enlaza. Publicamos un extracto breve; el artículo completo sigue en arXiv AI Papers.
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