53 citations · 220 across the 27 of their papers we have counts for
5 papers · 1 filter
Towards Real-Time Temporal Graph Learning
Deniz Gurevin, Mohsin Shan, Tong Geng +3
In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achi…
Towards Sparsification of Graph Neural Networks
Hongwu Peng, Deniz Gurevin, Shaoyi Huang +4
As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such models makes training and inference on graphs expensiv…
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Hongwu Peng, Shaoyi Huang, Shiyang Chen +8
Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Tong Geng, Tianqi Wang, Ang Li +2
Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is gen…