# A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

arXiv: https://arxiv.org/abs/2204.09579 · HTML: https://mlsanity.com/paper/2204.09579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 306 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 30 of 43)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Interpretation of Neural Networks is Fragile](https://mlsanity.com/paper/1710.10547/) (2017)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (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)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [Pythia: AI-assisted Code Completion System](https://mlsanity.com/paper/1912.00742/) (2019)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [Developing Synthesis Flows Without Human Knowledge](https://mlsanity.com/paper/1804.05714/) (2018)
- [IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2102.08138/) (2021)
- [High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing](https://mlsanity.com/paper/2201.06848/) (2022)
- [A Deep Reinforcement Learning Approach for Global Routing](https://mlsanity.com/paper/1906.08809/) (2019)
- [Learning to Design Circuits](https://mlsanity.com/paper/1812.02734/) (2018)
- [Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design](https://mlsanity.com/paper/1907.12952/) (2019)
- [PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network](https://mlsanity.com/paper/2011.13494/) (2020)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection](https://mlsanity.com/paper/2107.09130/) (2021)
- [BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks](https://mlsanity.com/paper/1907.10515/) (2019)
- [A general approach for identifying hierarchical symmetry constraints for analog circuit layout](https://mlsanity.com/paper/2010.00051/) (2020)
- [Optimizing Design Verification using Machine Learning: Doing better than Random](https://mlsanity.com/paper/1909.13168/) (2019)
- [SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation](https://mlsanity.com/paper/1809.10743/) (2018)
- [Placement Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.08445/) (2020)
- [Hardware Trojan Detection through Information Flow Security Verification](https://mlsanity.com/paper/1803.04102/) (2018)

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

- [Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2401.09479/) (2024)

## Abstract (arXiv)

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design; specifically the design of digital and analog integrated circuits~(ICs), where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time-to-market, and the growing number of ubiquitous interconnected devices (IoT). However, the security concerns and issues related to IC design have been highly overlooked. In this paper, we summarize the state-of-the-art in AL/ML for circuit design/optimization, security and engineering challenges, research in security-aware CAD/EDA, and future research directions and needs for using AI/ML for security-aware circuit design.