17 citations · 50 across the 12 of their papers we have counts for
4 papers · 1 filter
Low-Rank+Sparse Tensor Compression for Neural Networks
Cole Hawkins, Haichuan Yang, Meng Li +2
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices. Tensor…
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina +1
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing. Graph neural networks (GNNs) have achieved remarkable p…
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
Yao Chen, Cole Hawkins, Kaiqi Zhang +2
The deep neural network (DNN) based AI applications on the edge require both low-cost computing platforms and high-quality services. However, the limited memory, computing resource…
On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method
Kaiqi Zhang, Cole Hawkins, Xiyuan Zhang +2
Various hardware accelerators have been developed for energy-efficient and real-time inference of neural networks on edge devices. However, most training is done on high-performanc…