# Neural Combinatorial Logic Circuit Synthesis from Input-Output Examples

arXiv: https://arxiv.org/abs/2210.16606 · HTML: https://mlsanity.com/paper/2210.16606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Teaching Temporal Logics to Neural Networks](https://mlsanity.com/paper/2003.04218/) (2020)
- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis](https://mlsanity.com/paper/2110.11292/) (2021)
- [Neural Circuit Synthesis from Specification Patterns](https://mlsanity.com/paper/2107.11864/) (2021)
- [Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization](https://mlsanity.com/paper/2012.02530/) (2020)
- [FACT: Learning Governing Abstractions Behind Integer Sequences](https://mlsanity.com/paper/2209.09543/) (2022)
- [Periodic Extrapolative Generalisation in Neural Networks](https://mlsanity.com/paper/2209.10280/) (2022)

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

- [ShortCircuit: AlphaZero-Driven Circuit Design](https://mlsanity.com/paper/2408.09858/) (2024)

## Abstract (arXiv)

We propose a novel, fully explainable neural approach to synthesis of combinatorial logic circuits from input-output examples. The carrying advantage of our method is that it readily extends to inductive scenarios, where the set of examples is incomplete but still indicative of the desired behaviour. Our method can be employed for a virtually arbitrary choice of atoms - from logic gates to FPGA blocks - as long as they can be formulated in a differentiable fashion, and consistently yields good results for synthesis of practical circuits of increasing size. In particular, we succeed in learning a number of arithmetic, bitwise, and signal-routing operations, and even generalise towards the correct behaviour in inductive scenarios. Our method, attacking a discrete logical synthesis problem with an explainable neural approach, hints at a wider promise for synthesis and reasoning-related tasks.