The growing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators.
While machine learning-based autotuning methods have emerged as a promising solution to reduce manual effort, both template-based and template-free approaches suffer from prolonged tuning times due to the profiling of invalid configurations, which may result in runtime errors.
To address this issue, we propose ML2Tuner, a multi-level machine learning-guided autotuning technique designed to improve efficiency and robustness.
ML2Tuner introduces two key ideas: (1) a validity prediction model to filter out invalid configurations prior to profiling, and (2) an advanced performance prediction model that leverages hidden features extracted during the compilation process.
Experimental results on an extended VTA accelerator demonstrate that ML2Tuner achieves equivalent performance improvements using only 12.3% of the samples required by a TVM-like approach and reduces invalid profiling attempts by an average of 60.8%, highlighting its potential to enhance autotuning performance by filtering out invalid configurations.