Fangxin Liu

AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks pdf

Li Jiang, Yanzhi Wang, Changzhi Dai, 2020, ArXiv.

LHC: A Low-Power Heterogeneous Computing Method on Neural Network Accelerator

Tao Li, Cheng Gong, Ye Lu, 2019, 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS).

BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization

Fangxin Liu, Haibao Yu, Zhezhi He, 2021, ACM Trans. Reconfigurable Technol. Syst..

PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration

Li Jiang, Fangxin Liu, Xiaoyao Liang, 2021, 2021 58th ACM/IEEE Design Automation Conference (DAC).

TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval pdf

Mang Ye, Fangxin Liu, Zhigang Chang, 2021, ICMR.

SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network

Wenbo Zhao, Naifeng Jing, Li Jiang, 2021, 2021 IEEE 39th International Conference on Computer Design (ICCD).

DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification pdf

Zhengwei Qi, Chenggang Wu, Fangxin Liu, 2021, ArXiv.

IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration

Wenbo Zhao, Li Jiang, Fangxin Liu, 2021, ACM Great Lakes Symposium on VLSI.