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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
An Algorithm-Hardware Co-design Method for Convolutional Neural Networks
Researchers at GW have developed an algorithm-hardware co-design framework for Convolutional Neural Networks (CNN) directed towards mitigating the effects of computational irregularities in existing models. The framework disclosed allows for a reduced model size as to the associated system. For example, the algorithm disclosed utilizes centrosymmetric...
Published: 1/7/2022   |   Updated: 12/17/2021   |   Inventor(s): Jiajun Li, Ahmed Louri
Keywords(s):  
Category(s): Technology Classifications > Computers Electronics & Software > Computing Architecture, Technology Classifications > Computers Electronics & Software > Artificial Intelligence