ApproxANN: an approximate computing framework for artificial neural network. - Qian Zhang 020; Ting Wang 008; Ye Tian 010; Feng Yuan; Qiang Xu 001 (DATE, 2015)

ApproxMA: Approximate Memory Access for Dynamic Precision Scaling. - Ye Tian 010; Qian Zhang 020; Ting Wang 008; Feng Yuan; Qiang Xu 001 (ACM Great Lakes Symposium on VLSI, 2015)

ApproxEigen: An Approximate Computing Technique for Large-Scale Eigen-Decomposition. - Qian Zhang 020; Ye Tian 010; Ting Wang 008; Feng Yuan; Qiang Xu 001 (ICCAD, 2015)

ApproxMap: On task allocation and scheduling for resilient applications. - Juan Yi; Qian Zhang 020; Ye Tian 010; Ting Wang 008; Weichen Liu; Edwin Hsing-Mean Sha; Qiang Xu 001 (ASP-DAC, 2016)

On efficient message passing in energy harvesting based distributed system. - Ye Tian 010; Qiang Xu 001; Jason Xue 001 (ASP-DAC, 2017)

ApproxLUT: A novel approximate lookup table-based accelerator. - Ye Tian 010; Ting Wang 008; Qian Zhang 020; Qiang Xu 001 (ICCAD, 2017)

Lookup table allocation for approximate computing with memory under quality constraints. - Ye Tian 010; Qian Zhang 020; Ting Wang 008; Qiang Xu 001 (DATE, 2018)

D2NN: a fine-grained dual modular redundancy framework for deep neural networks. - Yu Li 007; Yannan Liu; Min Li 019; Ye Tian 010; Bo Luo; Qiang Xu 001 (ACSAC, 2019)

Lightweight prediction based big/little design for efficient neural network inference. - Ye Tian 010; Min Li 019; Qiang Xu 001 (SEC, 2019)

DeepDyve: Dynamic Verification for Deep Neural Networks. - Yu Li 007; Min Li 019; Bo Luo; Ye Tian 010; Qiang Xu 001 (CCS, 2020)

AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference. - Min Li 019; Yu Li 007; Ye Tian 010; Li Jiang 002; Qiang Xu 001 (DAC, 2021)