# CircuitVAE: Efficient and Scalable Latent Circuit Optimization

arXiv: https://arxiv.org/abs/2406.09535 · HTML: https://mlsanity.com/paper/2406.09535/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 13 Jun 2024.

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

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining](https://mlsanity.com/paper/2006.09191/) (2020)

## Built on by (most cited first, 4 of 4)

- [AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models](https://mlsanity.com/paper/2507.02598/) (2025)
- [PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization](https://mlsanity.com/paper/2507.06127/) (2025)
- [GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units](https://mlsanity.com/paper/2507.18989/) (2025)
- [PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer](https://mlsanity.com/paper/2511.19472/) (2025)

## Abstract (arXiv)

Automatically designing fast and space-efficient digital circuits is challenging because circuits are discrete, must exactly implement the desired logic, and are costly to simulate. We address these challenges with CircuitVAE, a search algorithm that embeds computation graphs in a continuous space and optimizes a learned surrogate of physical simulation by gradient descent. By carefully controlling overfitting of the simulation surrogate and ensuring diverse exploration, our algorithm is highly sample-efficient, yet gracefully scales to large problem instances and high sample budgets. We test CircuitVAE by designing binary adders across a large range of sizes, IO timing constraints, and sample budgets. Our method excels at designing large circuits, where other algorithms struggle: compared to reinforcement learning and genetic algorithms, CircuitVAE typically finds 64-bit adders which are smaller and faster using less than half the sample budget. We also find CircuitVAE can design state-of-the-art adders in a real-world chip, demonstrating that our method can outperform commercial tools in a realistic setting.