9 citations · 15 across the 6 of their papers we have counts for
6 papers
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…
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…
Accelerating Generative Neural Networks on Unmodified Deep Learning Processors -- A Software Approach
Dawen Xu, Ying Wang, Kaijie Tu +3
Generative neural network is a new category of neural networks and it has been widely utilized in applications such as content generation, unsupervised learning, segmentation and p…