17 citations · 27 across the 4 of their papers we have counts for
9 papers
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…
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination
Cole Hawkins, Xing Liu, Zheng Zhang
While post-training model compression can greatly reduce the inference cost of a deep neural network, uncompressed training still consumes a huge amount of hardware resources, run-…
Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models
Chunfeng Cui, Cole Hawkins, Zheng Zhang
Tensor methods have become a promising tool to solve high-dimensional problems in the big data era. By exploiting possible low-rank tensor factorization, many high-dimensional mode…