VLSI Design

VLSI design, the process of creating integrated circuits, is undergoing a transformation driven by the need for faster, more energy-efficient chips. Current research focuses on developing novel architectures, such as highly parallel, non-von Neumann designs, to improve performance and reduce power consumption, particularly for applications like AI accelerators and robotics. This involves leveraging machine learning techniques, including generative models like diffusion models and GANs, to augment limited training data for improved accuracy in performance prediction and design optimization, and employing sophisticated neural networks for tasks like congestion prediction. These advancements are crucial for enabling the development of next-generation electronic devices and systems.

Papers

September 28, 2024

Co-design of a novel CMOS highly parallel, low-power, multi-chip neural network accelerator
W Hokenmaier, R Jurasek, E Bowen, R Granger, D Odom
Co Design Chip Design Neural Network Accelerator Dual Network Complementary Metal Oxide Semiconductor AI Hardware VLSI Design

March 7, 2024

VLSI Architectures of Forward Kinematic Processor for Robotics Applications
Sourav Roy, Subhadeep Paul, Tapas Kumar Maiti
Field Programmable Gate Array Robotics Application Forward Kinematics Base Robot VLSI Design

October 15, 2023

Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
Prasha Srivastava, Pawan Kumar, Zia Abbas
Machine Learning Diffusion Model Synthetic Data Generative AI Study Feature Synthetic Data Generation Data Generation Digital Circuit VLSI Design

May 7, 2023

HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction
Yuxiang Zhao, Zhuomin Chai, Yibo Lin, Runsheng Wang, Ru Huang
Topological Perspective Branch Network Hybrid Network Congestion Prediction VLSI Design

February 15, 2023

Qualitative Data Augmentation for Performance Prediction in VLSI circuits
Prasha Srivastava, Pawan Kumar, Zia Abbas
Generative Adversarial Network Performance Prediction Electronic Design Automation Digital Circuit Data Augmentation Approach VLSI Design

August 1, 2022

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)
Zhuomin Chai, Yuxiang Zhao, Yibo Lin, Wei Liu, Runsheng Wang, Ru Huang
Machine Learning Open Source Machine Learning Application Electronic Design Automation VLSI Design

VLSI Design

Papers

Co-design of a novel CMOS highly parallel, low-power, multi-chip neural network accelerator

VLSI Architectures of Forward Kinematic Processor for Robotics Applications

Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation

HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction

Qualitative Data Augmentation for Performance Prediction in VLSI circuits

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)