5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator
Zhuofu Tao, Chen Wu, Yuan Liang +1
Graph convolutional networks (GCNs) have been introduced to effectively process non-euclidean graph data. However, GCNs incur large amounts of irregularity in computation and memor…
eess.SP2020
Low Precision Floating-point Arithmetic for High Performance FPGA-based CNN Acceleration
Chen Wu, Mingyu Wang, Xinyuan Chu +2
Low precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations:…
eess.SP2020★ 5 cited
Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks
Chen Wu, Mingyu Wang, Xiayu Li +3
Convolutional neural networks (CNNs) achieve state-of-the-art performance at the cost of becoming deeper and larger. Although quantization (both fixed-point and floating-point) has…