Context: Superoptimization is a synthesis technique that, given a loop-free sequence of instructions, searches
for an equivalent sequence that is optimal wrt. an objective function. Superoptimization of Ethereum smart
contracts aims at minimizing the size of their bytecode and the gas consumption of executing the contract’s
functions. The search for the optimal solution poses huge computational demands – as the search space to find
the optimal sequence is exponential on the given size-bound – being the main challenge for superoptimization
today to scale up to real, industrial software. Even if the underlying problem for finding the optimal solution is
decidable, practical tools often prioritize efficiency over completeness. This means they might be implemented
to find a sub-optimal solution or even time out.
Objective: This work aims at leveraging superoptimization to a real setting: Ethereum blockchain. This paper
proposes a neural-guided superoptimization (NGS) approach which incorporates deep neural networks using
(supervised) learning into superoptimization to improve scalability by predicting: (1) if a sequence is already
optimal and hence the search can be skipped; (2) the size-bound for the optimal solution in order to reduce
the search space.
Method: We have downloaded over 13,000 smart contracts deployed on the blockchain for training and testing
the machine learning models, and a disjoint set with 100 of the smart contracts with more transactions to prove
our scalability gains and impact for the Ethereum community.
Results: Incorporating DNNs resulted in a 16x overall speedup (12x for gas) with only 12% optimization loss
(14% for gas), or a 3-4x speedup with no optimization loss. For the 100 analyzed contracts, this approach
reduced the average compilation time to 3 min per contract and achieved monetary savings of $1.24M.
Conclusions: The integration of machine learning models mitigates several limitations of traditional superoptimization
by drastically reducing execution times while maintaining most of the original optimization
gains.