mlsanityData to 7 Oct 2026

AI/ML Algorithms and Applications in VLSI Design and Technology

arXiv 2202.10015 ↗First posted 21 Feb 2022cs.LGcited by 5

Not on the map. Why?

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 420 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

25 of 420 references.

References · 25

  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  2. 2014Conditional Generative Adversarial Netscited by 2,152
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  5. 2019An Introduction to Variational Autoencoderscited by 997
  6. 2020Chip Placement with Deep Reinforcement Learningcited by 83
  7. 2021Machine Learning for Electronic Design Automation: A Surveycited by 71
  8. 2018A Comprehensive Analysis of Deep Regressioncited by 48
  9. 2020Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insightscited by 27
  10. 2020PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Networkcited by 16
  11. 2018Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimizationcited by 15
  12. 2019Reinforcement Learning Driven Heuristic Optimizationcited by 12
13 more
  1. 2019Optimizing Design Verification using Machine Learning: Doing better than Randomcited by 8
  2. 2020Logic Synthesis Meets Machine Learning: Trading Exactness for Generalizationcited by 8
  3. 2020Placement Optimization with Deep Reinforcement Learningcited by 7
  4. 2018A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasetscited by 5
  5. 2018Data Efficient Lithography Modeling with Transfer Learning and Active Data Selectioncited by 4
  6. 2017Generative Mixture of Networkscited by 3
  7. 2020A Machine Learning Pipeline Stage for Adaptive Frequency Adjustmentcited by 3
  8. 2018Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learningcited by 2
  9. 2019Automatic Layout Generation with Applications in Machine Learning Engine Evaluationcited by 2
  10. 2019VLSI Mask Optimization: From Shallow To Deep Learningcited by 2
  11. 2014EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulationcited by 1
  12. 2018Systimator: A Design Space Exploration Methodology for Systolic Array based CNNs Acceleration on the FPGA-based Edge Nodescited by 1
  13. 2021A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platformscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Qualitative Data Augmentation for Performance Prediction in VLSI circuitscited by 1
  2. 2023Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generationcited by 0
  3. 2024The Evolution and Future Perspectives of Artificial Intelligence Generated Contentcited by 0
  4. 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domainscited by 0
  5. 2026cktFormer: Transformer-Based Approach for Automated Analog Circuit Designcited by 0