# The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration

arXiv: https://arxiv.org/abs/2502.17936 · HTML: https://mlsanity.com/paper/2502.17936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization](https://mlsanity.com/paper/2409.06077/) (2024)

## Built on by (most cited first, 1 of 1)

- [GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units](https://mlsanity.com/paper/2507.18989/) (2025)

## Abstract (arXiv)

This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14x synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor.