IEEE

HS-GCN: A High-Performance, Sustainable, and Scalable Chiplet-Based Accelerator for Graph Convolutional Network Inference

Abstract: Graph Convolutional Networks (GCNs) have been proposed to extend machine learning techniques for graph-related applications. A typical GCN model consists of multiple layers, each including ...
Frontiers

Artificial intelligence and deep learning for neural data analysis

Artificial intelligence and deep learning have revolutionized the field of neural data analysis in recent years. The explosion of complex, high-dimensional ...
Frontiers

Advancing Computational Material Science and Mechanics through Integrated Deep Learning Models

The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...
IEEE

HGPplace: Hierarchical Chip Layout Optimization Combining Graph Convolutional Networks and Polygon-Aware Placement

Abstract: Since the advent of Electronic Design Automation (EDA) in integrated circuit (IC) design, layout optimization has become a critical factor in determining chip performance, power consumption, ...