# DeepTPI: Test Point Insertion with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2206.06975 · HTML: https://mlsanity.com/paper/2206.06975/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs](https://mlsanity.com/paper/1904.11088/) (2019)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [Chip Placement with Deep Reinforcement Learning](https://mlsanity.com/paper/2004.10746/) (2020)
- [Directed Acyclic Graph Neural Networks](https://mlsanity.com/paper/2101.07965/) (2021)
- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)

## Built on by (most cited first, 10 of 10)

- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [DeepGate3: Towards Scalable Circuit Representation Learning](https://mlsanity.com/paper/2407.11095/) (2024)
- [DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale](https://mlsanity.com/paper/2502.01681/) (2025)
- [Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs](https://mlsanity.com/paper/2405.06758/) (2024)
- [DeepSeq: Deep Sequential Circuit Learning](https://mlsanity.com/paper/2302.13608/) (2023)
- [DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations](https://mlsanity.com/paper/2411.00530/) (2024)
- [DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning](https://mlsanity.com/paper/2502.06816/) (2025)
- [HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation](https://mlsanity.com/paper/2207.11312/) (2022)
- [Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning](https://mlsanity.com/paper/2509.20968/) (2025)
- [GNN-based Path-aware multi-view Circuit Learning for Technology Mapping](https://mlsanity.com/paper/2601.14286/) (2026)

## Abstract (arXiv)

Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinforcement learning (DRL), named DeepTPI. Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement. Specifically, we model circuits as directed graphs and design a graph-based value network to estimate the action values for inserting different test points. The policy of the DRL agent is defined as selecting the action with the maximum value. Moreover, we apply the general node embeddings from a pre-trained model to enhance node features, and propose a dedicated testability-aware attention mechanism for the value network. Experimental results on circuits with various scales show that DeepTPI significantly improves test coverage compared to the commercial DFT tool. The code of this work is available at https://github.com/cure-lab/DeepTPI.