4 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…