9 citations · 18 across the 11 of their papers we have counts for
11 papers
Cross-Layer Optimization for Fault-Tolerant Deep Learning
Qing Zhang, Cheng Liu, Bo Liu +4
Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avio…
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs
Erjing Luo, Haitong Huang, Cheng Liu +5
Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computi…
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
Xinghua Xue, Cheng Liu, Bo Liu +6
Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought…
Soft Error Reliability Analysis of Vision Transformers
Xinghua Xue, Cheng Liu, Ying Wang +5
Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) an…
Statistical Modeling of Soft Error Influence on Neural Networks
Haitong Huang, Xinghua Xue, Cheng Liu +5
Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is c…
Taming Process Variations in CNFET for Efficient Last Level Cache Design
Dawen Xu, Zhuangyu Feng, Cheng Liu +5
Carbon nanotube field-effect transistors (CNFET) emerge as a promising alternative to CMOS transistors for the much higher speed and energy efficiency, which makes the technology p…
R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors
Dawen Xu, Meng He, Cheng Liu +5
AIoT processors fabricated with newer technology nodes suffer rising soft errors due to the shrinking transistor sizes and lower power supply. Soft errors on the AIoT processors pa…
Energy-Efficient Accelerator Design for Deformable Convolution Networks
Dawen Xu, Cheng Chu, Cheng Liu +4
Deformable convolution networks (DCNs) proposed to address the image recognition with geometric or photometric variations typically involve deformable convolution that convolves on…
HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning
Cheng Liu, Cheng Chu, Dawen Xu +5
Hardware faults on the regular 2-D computing array of a typical deep learning accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches ty…
MPC-CSAS: Multi-Party Computation for Real-time Privacy-preserving Speed Advisory Systems
Mingming Liu, Long Cheng, Yingqi Gu +3
As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for spe…
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
Shengwen Liang, Ying Wang, Cheng Liu +3
Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean data structures and have been proved powerful in various application domains such as social netw…