most citedEnergy-Efficient Accelerator Design for Deformable Convolution Networks

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR2021

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…

cs.AR2021

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…

cs.AR20214 cited

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…

cs.AR20212 cited

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…

eess.SY2021

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…