Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning
SB Atitallah, CB Rabah, M Driss, W Boulila… - ar** complex Graph Neural
Network (GNN) models, allowing for concise representations of edge and vertex-wise …
Network (GNN) models, allowing for concise representations of edge and vertex-wise …
A Design Framework of Heterogeneous Approximate DCIM-Based Accelerator for Energy-Efficient NN Processing
K Lee, H Lee, J Park - … Transactions on Circuits and Systems I …, 2025 - ieeexplore.ieee.org
Static random-access memory (SRAM) based digital compute-in-memory (DCIM) provides
error-resilient computation at the expense of considerable power overhead of adder tree. In …
error-resilient computation at the expense of considerable power overhead of adder tree. In …
Pruned Graph Neural Networks for Efficient Edge Classification and Fast Inference
H Paschke, J Gaboriault-Whitcomb… - … Conference on Big …, 2024 - ieeexplore.ieee.org
Optimizing Graph Neural Networks (GNNs) inference is important for various applications,
including High-Energy Physics experiments, natural language processing, and point cloud …
including High-Energy Physics experiments, natural language processing, and point cloud …
High-Performance, Energy-Efficient, and Scalable Accelerator Design for Emerging Machine Learning Applications
L Yin - 2024 - stars.library.ucf.edu
The use of machine learning (ML) is pervasive in numerous application domains, such as
autonomous driving, scientific computing, robotics, and among others. However, the …
autonomous driving, scientific computing, robotics, and among others. However, the …