# AI/ML Algorithms and Applications in VLSI Design and Technology

arXiv: https://arxiv.org/abs/2202.10015 · HTML: https://mlsanity.com/paper/2202.10015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 420 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [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)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)
- [Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights](https://mlsanity.com/paper/2007.00864/) (2020)
- [PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network](https://mlsanity.com/paper/2011.13494/) (2020)
- [Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization](https://mlsanity.com/paper/1811.04770/) (2018)
- [Reinforcement Learning Driven Heuristic Optimization](https://mlsanity.com/paper/1906.06639/) (2019)
- [Optimizing Design Verification using Machine Learning: Doing better than Random](https://mlsanity.com/paper/1909.13168/) (2019)
- [Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization](https://mlsanity.com/paper/2012.02530/) (2020)
- [Placement Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.08445/) (2020)
- [A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets](https://mlsanity.com/paper/1803.04783/) (2018)
- [Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection](https://mlsanity.com/paper/1807.03257/) (2018)
- [Generative Mixture of Networks](https://mlsanity.com/paper/1702.03307/) (2017)
- [A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment](https://mlsanity.com/paper/2007.01820/) (2020)
- [Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning](https://mlsanity.com/paper/1807.06446/) (2018)
- [Automatic Layout Generation with Applications in Machine Learning Engine Evaluation](https://mlsanity.com/paper/1912.05796/) (2019)
- [VLSI Mask Optimization: From Shallow To Deep Learning](https://mlsanity.com/paper/1912.07254/) (2019)
- [EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation](https://mlsanity.com/paper/1402.2904/) (2014)
- [Systimator: A Design Space Exploration Methodology for Systolic Array based CNNs Acceleration on the FPGA-based Edge Nodes](https://mlsanity.com/paper/1901.04986/) (2018)
- [A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms](https://mlsanity.com/paper/2103.04780/) (2021)

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

- [Qualitative Data Augmentation for Performance Prediction in VLSI circuits](https://mlsanity.com/paper/2302.07566/) (2023)
- [Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation](https://mlsanity.com/paper/2310.10691/) (2023)
- [The Evolution and Future Perspectives of Artificial Intelligence Generated Content](https://mlsanity.com/paper/2412.01948/) (2024)
- [Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains](https://mlsanity.com/paper/2506.14567/) (2025)
- [cktFormer: Transformer-Based Approach for Automated Analog Circuit Design](https://mlsanity.com/paper/2609.36752/) (2026)

## Abstract (arXiv)

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.