Deep Symbolic Superoptimization Without Human Knowledge

Deep symbolic superoptimization refers to the task of applying deep learning methods to simplify symbolic expressions. Existing approaches either perform supervised training on human-constructed datasets that define equivalent expression pairs, or apply reinforcement learning with human-defined equivalent transformation actions. In short, almost all existing methods rely on human knowledge to define equivalence, which suffers from large labeling cost and learning bias. We thus propose HISS, a reinforcement learning framework for symbolic superoptimization that keeps humans outside the loop. HISS introduces a tree-LSTM encoder-decoder network with attention to ensure tractable learning. Our experiments show that HISS can discover more simplification rules than existing human-dependent methods, and can learn meaningful embeddings for symbolic expressions, which are indicative of equivalence. 1