Haiyue Song, Li Jiang, Chengwen Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang and Qiang Xu. Accepted by ACM/IEEE Accepted by ACM/IEEE (GLSVLSI 2020, CCF-C). ESNreram: An Energy-Efficient Sparse Neural Network Based on Resistive Random-Access Memory. Zhuoran Song, Yilong Zhao, Yanan Sun, Xiaoyao Liang and Li Jiang. ACM Transactions on Design Automation of Electronic Systems (TODAES 2019, CCF-B). Energy-Efficient and Quality-Assured Approximate Computing Framework Using a Co-Training Method. Accepted by ACM/IEEE Design Automation & Test in Europe Conference and Exhibition (DATE 2019, CCF-B). Approximate Random Dropout for DNN training acceleration in GPGPU. Zhuoran Song, Dongyu Ru, Ru Wang, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, and Li Jiang.
Accepted by IEEE International Conference on High Performance Computing and Communications (HPCC 2020, CCF-C). PRArch: Pattern-Based Reconfigurable Architecture for Deep Neural Network Acceleration. Zhaoming Jiang, Zhuoran Song, Naifeng Jing and Xiaoyao Liang. Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2020, CCF-A). ITT-RNA: Imperfection Tolerable Training for RRAM-Crossbar based Deep Neural-network Accelerator. Zhuoran Song, Lerong Chen, Tianjian Li, Naifeng Jing, Xiaoyao Liang, Yanan Sun and Li Jiang. Accepted by Design Automation Conference (DAC 2020, CCF-A). GPNPU: Enabling Efficient Hardware-Based Direct Convolution with Multi-Precision Support in GPU Tensor Cores. Zhuoran Song, Jianfei Wang, Tianjian Li, Li Jiang, Jing Ke, Xiaoyao Liang and Naifeng Jing. Accepted by IEEE/ACM International Symposium on Computer Architecture (ISCA 2020, CCF-A). DRQ: Dynamic Region-Based Quantization for Deep Neural Network Acceleration.
Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing and Xiaoyao Liang. Accepted by IEEE/ACM International Symposium on Microarchitecture (MICRO 2020, CCF-A). VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration. Zhuoran Song, Feiyang Wu, Xueyuan Liu, Naifeng Jing and Xiaoyao Liang. Accepted by IEEE International Symposium on Circuits and Systems (ISCAS 2021, CCF-C). ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator. Zhuoran Song*, Dongyue Li*, Zhezhi He, Xiaoyao Liang and Li Jiang. Accepted by 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA 2022, CCF-C). PIPArch: Programmable Image Processing Architecture Using Sliding Array. Accepted by ACM/IEEE Design Automation & Test in Europe Conference and Exhibition (DATE 2022, CCF-B). DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He and Li Jiang. Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2022, CCF-A). PASGCN: An ReRAM-Based PIM Design for GCN with Adaptively Sparsified Graphs. Tao Yang, Dongyue Li, Fei Ma, Zhuoran Song, Yilong Zhao, Jiaxi Zhang, Fangxin Liu and Li Jiang. Accepted by Design Automation Conference (DAC 2022, CCF-A). E 2SR: An End-to-End Video CODEC Assisted System for Super Resolution Acceleration.
Zhuoran Song, Zhongkai Yu, Naifeng Jing and Xiaoyao Liang. Accepted by Transactions on Design Automation of Electronic Systems (TODAES 2022, CCF-B). E 2-VOR: An End-to-End En/Decoder Architecture for Efficient Video Object Recognition. Zhuoran Song, Naifeng Jing and Xiaoyao Liang. Accepted by 2022 IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2022, CCF-B). Gzippo: Highly-compact Processing-In-Memory Graph Accelerator Alleviating Sparsity and Redundancy. Xing Li, Rachata Ausavarungnirun, Xiao Liu, Xueyuan Liu, Xuan Zhang, Heng Lu, Zhuoran Song, Naifeng Jing and Xiaoyao Liang. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2022, CCF-B). GCNTrain: A Unified and Efficient Accelerator for Graph Convolutional Neural Networks Training. Heng Lu, Zhuoran Song*, Xing Li, Naifeng Jing and Xiaoyao Liang.
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