3 citations · 5 across the 6 of their papers we have counts for
7 papers
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…
Fault-Tolerant Deep Learning: A Hierarchical Perspective
Cheng Liu, Zhen Gao, Siting Liu +3
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications s…
Winograd Convolution: A Perspective from Fault Tolerance
Xinghua Xue, Haitong Huang, Cheng Liu +3
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than…
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…
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…