2024

Xueyuan Liu, Zhuoran Song*, Hao Chen, Xing Li, and Xiaoyao Liang. MoC: A Morton-Code-Based Fine-Grained Quantization for Accelerating Point Cloud Neural Networks. Accepted by Design Automation Conference (DAC 2024, CCF-A).

Xuhang Wang, Zhuoran Song*, and Xiaoyao Liang. InterArch: Video Transformer Acceleration via Inter-Feature Deduplication with Cube-based Dataflow. Accepted by Design Automation Conference (DAC 2024, CCF-A).

Zhuoran Song, Chunyu Qi, Yuanzheng Yao, Peng Zhou, Yanyi Zi, Nan Wang, and Xiaoyao Liang. TSAcc: An Efficient Tempo-Spatial Similarity Aware Accelerator for Attention Acceleration. Accepted by Design Automation Conference (DAC 2024, CCF-A).

Zhuoran Song, Chunyu Qi, Fangxin Liu, Naifeng Jing, and Xiaoyao Liang. CMC: Video Transformer Acceleration via CODEC Assisted Matrix Condensing. Accepted by ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024, CCF-A, Accept rate=14%).

Xueyuan Liu, Zhuoran Song*, Guohao Dai, Gang Li, Can Xiao, Yan Xiang, Dehui Kong, Ke Xu and Xiaoyao Liang. FusionArch: A Fusion-Based Accelerator for Point-Based Point Cloud Neural Networks. Accepted by Design, Automation, and Test in Europe (DATE 2024, CCF-B). Best Paper Award.

Xueyuan Liu, Zhuoran Song*, Xiang Liao, Xing Li, Tao Yang, Fangxin Liu and Xiaoyao Liang. Sava: A Spatial- and Value-Aware Accelerator for Point Cloud Transformers. Accepted by Design, Automation, and Test in Europe (DATE 2024, CCF-B).

Fangxin Liu, Ning Yang, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang. SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding. Accepted by High Performance Computer Architecture (HPCA 2024, CCF-A).

2023

Zhuoran Song, Wanzhen Liu, Tao Yang, Fangxin Liu, Naifeng Jing, and Xiaoyao Liang. A Point Cloud Video Recognition Acceleration Framework Based on Tempo-Spatial Information. Accepted by IEEE Transactions on Parallel and Distributed Systems (TPDS 2023, CCF-A). [paper] [cite]

Chunyu Qi, Zilong Li, Zhuoran Song* and Xiaoyao Liang. ViTframe: Vision Transformer Acceleration via Informative Frame Selection for Video Recognition. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2023, CCF-B). [paper] [cite]

Xuhang Wang, Zhuoran Song* and Xiaoyao Liang. RealArch: A Real-Time Scheduler for Mapping Multi-Tenant DNNs on Multi-Core Accelerators. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2023, CCF-B). [paper] [cite]

Xuan Zhang, Zhuoran Song*, Xing Li, Zhezhi He, Li Jiang, Naifeng Jing and Xiaoyao Liang. HyAcc: A Hybrid CAM-MAC RRAM-based Accelerator for Recommendation Model. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2023, CCF-B). [paper] [cite]

Xuhang Wang, Zhuoran Song*, Qiyue Huang and Xiaoyao Liang. DEQ: Dynamic Element-wise Quantization for Efficient Attention Architecture. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2023, CCF-B). [paper] [cite]

Xiaolong Lin, Gang Li, Zizhao Liu, Yadong Liu, Fan Zhang, Zhuoran Song, Naifeng Jing, and Xiaoyao Liang. AdaS: A Fast and Energy-Efficient CNN Accelerator Exploiting Bit-Sparsity. Accepted by Design Automation Conference (DAC 2023, CCF-A). [paper] [cite]

Zhuoran Song, Heng Lu, Gang Li, Li Jiang, Naifeng Jing and Xiaoyao Liang. PRADA: Point Cloud Recognition Acceleration via Dynamic Approximation. Accepted by Design, Automation and Test in Europe Conference (DATE 2023, CCF-B). Best Paper Award. [paper] [cite]

2022

Zhuoran Song, Heng Lu, Li Jiang, Naifeng Jing and Xiaoyao Liang. Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration Framework. Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2022, CCF-A). [paper] [cite]

Heng Lu, Zhuoran Song*, Xing Li, Naifeng Jing and Xiaoyao Liang. GCNTrain: A Unified and Efficient Accelerator for Graph Convolutional Neural Networks Training. Accepted by 40th IEEE International Conference on Computer Design (ICCD 2022, CCF-B). [paper] [cite]

Gang Li, Weixiang Xu, Zhuoran Song, Naifeng Jing, Jian Chen and Xiaoyao Liang. Ristretto: An Atomized Processing Architecture for Sparsity-Condensed Stream Flow in CNN. Accepted by 55th ACM/IEEE International Symposium on Microarchitecture (MICRO 2022, CCF-A). [paper] [cite]

Xing Li, Rachata Ausavarungnirun, Xiao Liu, Xueyuan Liu, Xuan Zhang, Heng Lu, Zhuoran Song, Naifeng Jing and Xiaoyao Liang. Gzippo: Highly-compact Processing-In-Memory Graph Accelerator Alleviating Sparsity and Redundancy. Accepted by 2022 IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2022, CCF-B). [paper] [cite]

Zhuoran Song, Naifeng Jing and Xiaoyao Liang. E²-VOR: An End-to-End En/Decoder Architecture for Efficient Video Object Recognition. Accepted by Transactions on Design Automation of Electronic Systems (TODAES 2022, CCF-B). [paper] [cite]

Zhuoran Song, Zhongkai Yu, Naifeng Jing and Xiaoyao Liang. E²SR: An End-to-End Video CODEC Assisted System for Super Resolution Acceleration. Accepted by Design Automation Conference (DAC 2022, CCF-A). [paper] [cite]

Tao Yang, Dongyue Li, Fei Ma, Zhuoran Song, Yilong Zhao, Jiaxi Zhang, Fangxin Liu and Li Jiang. PASGCN: An ReRAM-Based PIM Design for GCN with Adaptively Sparsified Graphs. Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2022, CCF-A). [paper] [cite]

Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He and Li Jiang. DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. Accepted by ACM/IEEE Design Automation & Test in Europe Conference and Exhibition (DATE 2022, CCF-B). Best Paper Nomination. [paper] [cite]

Feiyang Wu, Zhuoran Song, Jing Ke, Li Jiang, Naifeng Jing and Xiaoyao Liang. PIPArch: Programmable Image Processing Architecture Using Sliding Array. 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). [paper] [cite]

2021

宋卓然，蒋力。深度神经网络专用架构与压缩技术演进。中国计算机学会通讯，2021年第3期。

Zhuoran Song=, Dongyue Li=, Zhezhi He, Xiaoyao Liang and Li Jiang. ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator. Accepted by IEEE International Symposium on Circuits and Systems (ISCAS 2021, CCF-C). [paper] [cite]

2020

Zhuoran Song, Feiyang Wu, Xueyuan Liu, Naifeng Jing and Xiaoyao Liang. VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration. Accepted by IEEE/ACM International Symposium on Microarchitecture (MICRO 2020, CCF-A). [paper] [cite]

Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing and Xiaoyao Liang. DRQ: Dynamic Region-Based Quantization for Deep Neural Network Acceleration. Accepted by IEEE/ACM International Symposium on Computer Architecture (ISCA 2020, CCF-A). [paper] [cite]

Zhuoran Song, Jianfei Wang, Tianjian Li, Li Jiang, Jing Ke, Xiaoyao Liang and Naifeng Jing. GPNPU: Enabling Efficient Hardware-Based Direct Convolution with Multi-Precision Support in GPU Tensor Cores. Accepted by Design Automation Conference (DAC 2020, CCF-A). [paper] [cite]

Zhuoran Song, Lerong Chen, Tianjian Li, Naifeng Jing, Xiaoyao Liang, Yanan Sun and Li Jiang. ITT-RNA: Imperfection Tolerable Training for RRAM-Crossbar based Deep Neural-network Accelerator. Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2020, CCF-A). [paper] [cite]

Zhaoming Jiang, Zhuoran Song, Naifeng Jing and Xiaoyao Liang. PRArch: Pattern-Based Reconfigurable Architecture for Deep Neural Network Acceleration. Accepted by IEEE International Conference on High Performance Computing and Communications (HPCC 2020, CCF-C). [paper] [cite]

Zhuoran Song, Yilong Zhao, Yanan Sun, Xiaoyao Liang and Li Jiang. ESNreram: An Energy-Efficient Sparse Neural Network Based on Resistive Random-Access Memory. Accepted by ACM/IEEE Accepted by ACM/IEEE (GLSVLSI 2020, CCF-C). [paper] [cite]

2019

Zhuoran Song, Dongyu Ru, Ru Wang, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, and Li Jiang. Approximate Random Dropout for DNN training acceleration in GPGPU. Accepted by ACM/IEEE Design Automation & Test in Europe Conference and Exhibition (DATE 2019, CCF-B). [paper] [cite]

Li Jiang=, Zhuoran Song=, Song H, et al. Energy-Efficient and Quality-Assured Approximate Computing Framework Using a Co-Training Method[J]. ACM Transactions on Design Automation of Electronic Systems (TODAES 2019, CCF-B). [paper] [cite]

2018

Haiyue Song, Li Jiang, Chengwen Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang and Qiang Xu. Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators. Accepted by ACM/IEEE International Conference on Computer-Aided Design (ICCAD 2018, CCF-B). [paper] [cite]