14 citations · 23 across the 5 of their papers we have counts for
5 papers
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Sung-En Chang, Yanyu Li, Mengshu Sun +2
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model com…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +5
Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edg…
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Kaidi Xu, Sijia Liu, Pin-Yu Chen +4
Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making poten…
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency
Zhengang Li, Yifan Gong, Xiaolong Ma +6
Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations. Previous works in this area leave great space…