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Conference Paper
ACLTuner: A Profiling-Driven Fast Tuning to Optimized Deep Learning Inference
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- Authors
- Yongin Kwon, Joo Hyoung Cha, Jubin Lee, Misun Yu, Jeman Park, Jemin Lee
- Issue Date
- 2023-12
- Citation
- Conference on Neural Information Processing Systems (NeurIPS) 2023 : Workshop, pp.1-12
- Language
- English
- Type
- Conference Paper
- Abstract
- Deep learning has expanded its footprint across diverse domains. The performance of these computations hinges on the interplay between deep learning compilers and inference libraries. While compilers adapt efficiently to new deep learning operations or models, their tuning processes are too time-consuming. In contrast, inference libraries offer quick execution but with adaptability limitations. To address these challenges, we propose ACLTuner, which optimizes execution configurations using existing inference library kernels. ACLTuner identifies and assigns the optimal kernel through targeted device profiling. Compared to ArmNN, AutoTVM, Ansor, ONNXRuntime, and TFLite, ACLTuner not only achieves up to 2.0x faster execution time across seven deep learning models, but also reduces the average tuning time by 95%.
- KSP Keywords
- deep learning(DL), deep learning models, execution time, optimal kernel, tuning time