# Automating Constraint-Aware Datapath Optimization using E-Graphs

arXiv: https://arxiv.org/abs/2303.01839 · HTML: https://mlsanity.com/paper/2303.01839/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [A Dataflow Compiler for Efficient LLM Inference using Custom Microscaling Formats](https://mlsanity.com/paper/2307.15517/) (2023)
- [e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving](https://mlsanity.com/paper/2508.13020/) (2025)
- [SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR](https://mlsanity.com/paper/2308.07654/) (2023)
- [Optimizing Tensor Computation Graphs with Equality Saturation and Monte Carlo Tree Search](https://mlsanity.com/paper/2410.05534/) (2024)
- [Revisit Choice Network for Synthesis and Technology Mapping](https://mlsanity.com/paper/2508.14068/) (2025)

## Abstract (arXiv)

Numerical hardware design requires aggressive optimization, where designers exploit branch constraints, creating optimization opportunities that are valid only on a sub-domain of input space. We developed an RTL optimization tool that automatically learns the consequences of conditional branches and exploits that knowledge to enable deep optimization. The tool deploys custom built program analysis based on abstract interpretation theory, which when combined with a data-structure known as an e-graph simplifies complex reasoning about program properties. Our tool fully-automatically discovers known floating-point architectures from the computer arithmetic literature and out-performs baseline EDA tools, generating up to 33% faster and 41% smaller circuits.