# DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations

arXiv: https://arxiv.org/abs/2411.00530 · HTML: https://mlsanity.com/paper/2411.00530/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Learning to Decompose and Disentangle Representations for Video Prediction](https://mlsanity.com/paper/1806.04166/) (2018)
- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs](https://mlsanity.com/paper/1904.11088/) (2019)
- [Directed Acyclic Graph Neural Networks](https://mlsanity.com/paper/2101.07965/) (2021)
- [DeepGate: Learning Neural Representations of Logic Gates](https://mlsanity.com/paper/2111.14616/) (2021)
- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks](https://mlsanity.com/paper/2303.08256/) (2023)
- [Transformers over Directed Acyclic Graphs](https://mlsanity.com/paper/2210.13148/) (2022)
- [DeepSAT: An EDA-Driven Learning Framework for SAT](https://mlsanity.com/paper/2205.13745/) (2022)
- [PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs](https://mlsanity.com/paper/2201.10114/) (2022)
- [DeepTPI: Test Point Insertion with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.06975/) (2022)
- [DeepSeq: Deep Sequential Circuit Learning](https://mlsanity.com/paper/2302.13608/) (2023)

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

- [GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment](https://mlsanity.com/paper/2504.09485/) (2025)
- [LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration](https://mlsanity.com/paper/2608.27184/) (2026)
- [Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Network](https://mlsanity.com/paper/2508.11991/) (2025)
- [TRACE: Learning to Compute on Circuit Graphs](https://mlsanity.com/paper/2509.21886/) (2025)
- [Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation](https://mlsanity.com/paper/2605.08291/) (2026)

## Abstract (arXiv)

Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One notable work, DeepSeq, has pioneered sequential circuit learning by encoding temporal correlations. However, it suffers from significant limitations including prolonged execution times and architectural inefficiencies. To address these issues, we introduce DeepSeq2, a novel framework that enhances the learning of sequential circuits, by innovatively mapping it into three distinct embedding spaces-structure, function, and sequential behavior-allowing for a more nuanced representation that captures the inherent complexities of circuit dynamics. By employing an efficient Directed Acyclic Graph Neural Network (DAG-GNN) that circumvents the recursive propagation used in DeepSeq, DeepSeq2 significantly reduces execution times and improves model scalability. Moreover, DeepSeq2 incorporates a unique supervision mechanism that captures transitioning behaviors within circuits more effectively. DeepSeq2 sets a new benchmark in sequential circuit representation learning, outperforming prior works in power estimation and reliability analysis.