|
A Flexible and Energy-Efficient Accelerator for Graph Convolutional Neural Networks
Researchers at GW have invented a flexible and energy-efficient accelerator for graph convolutional neural networks (GCN). First, the novel accelerator design disclosed shows highly enhanced performance in comparison to existing accelerators. For example, the accelerator is capable of simultaneously improving resource utilization and data movement in...
Published: 1/7/2022
|
Updated: 12/17/2021
|
Inventor(s): Ahmed Louri, Jiajun Li
Keywords(s):
Category(s): Technology Classifications > Computers Electronics & Software > Artificial Intelligence, Technology Classifications > Computers Electronics & Software > Computing Architecture
|
