Injective Domain Knowledge in Neural Networks for Transprecision Computing
This work addresses the challenge of scarce data or complex functions in machine learning for transprecision computing, offering a domain-specific solution that is incremental in nature.
The paper tackled the problem of precision tuning for transprecision computing applications by integrating domain knowledge into neural networks, resulting in an average accuracy improvement of around 38% compared to purely data-driven models.
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learning problems that cannot be easily solved relying on pure data, e.g. scarce data or very complex functions to be approximated. Fortunately, in many contexts domain knowledge is explicitly available and can be used to train better ML models. This paper studies the improvements that can be obtained by integrating prior knowledge when dealing with a non-trivial learning task, namely precision tuning of transprecision computing applications. The domain information is injected in the ML models in different ways: I) additional features, II) ad-hoc graph-based network topology, III) regularization schemes. The results clearly show that ML models exploiting problem-specific information outperform the purely data-driven ones, with an average accuracy improvement around 38%.