Journal Publications

  • [CACM] Shaoshan Liu, Xiaolong Ma, Wei Niu, Bin Ren, Xipeng Shen, Yanzhi Wang, Pu Zhao, “CoCoPIE: Making Mobile AI Sweet As PIE — Compression-Compilation Co-Design Goes a Long Way” to be appear in the Communications of the ACM, 2020. (authors in alphabetical order).
  • [TNNLS] Xiaolong Ma*, Sheng Lin*, Shaokai Ye, Zhezhi He, Linfeng Zhang, Geng Yuan, Sia Huat Tan, Zhengang Li, Deliang Fan, Xuehai Qian, Xue Lin, Kaisheng Ma, Yanzhi Wang, “Rethinking the Value of DNN Weight Sparsity on Hardware: Is It Truly Beneficial?”, in IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 11.68), 2020.
  • [TNNLS] Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Xiaolong Ma, Ning Liu, Linfeng Zhang, Jian Tang, Kaisheng Ma, Xue Lin, Makan Fardad, Yanzhi Wang, “StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs”, in IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 11.68), 2020.

Conference publications

  • [20’GLSVLSI] Yifan Gong, Zheng Zhan, Zhengang Li, Wei Niu, Xiaolong Ma, Wenhao Wang, Bin Ren, Caiwen Ding, Xue Lin, Xiaolin Xu, Yanzhi Wang, “A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework”, in Proceedings of the 2020 on Great Lakes Symposium on VLSI (GLVLSI 2020).
  • [20’SOCC] Geng Yuan*, Xiaolong Ma*, Sheng Lin, Zhengang Li, Jieren Deng, Caiwen Ding, “A DNN Compression Framework for SOT-MRAM-Based Processing-In-Memory Engine”, in Proceeding of the 33rd IEEE International System-on-chip Conference (SOCC 2020).
  • [20’PACT] Masuma Akter Rumi, Xiaolong Ma, Yanzhi Wang, Peng Jiang, “Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning”, in Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT 2020, acceptance rate: 25%).
  • [20’ECCV] Xiaolong Ma*, Wei Niu*, Tianyun Zhang, Sijia Liu, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang, “An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices”, in Proceedings of the 16th European Conference on Computer Vision (ECCV 2020, acceptance rate: 27%).
  • [20’ICS] Runbin Shi, Peiyan Dong, Tong Geng, Yuhao Ding, Xiaolong Ma, Martin Herbordt, Ang Li, Hayden So, and Yanzhi Wang, “CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks”, in Proceeding of the International Conference on Supercomputing (ICS 2020).
  • [Under review] Xiaolong Ma*, Zhengang Li*, Yifan Gong, Tianyun Zhang, Wei Niu, Zheng Zhan, Pu Zhao, Jian Tang, Xue Lin, Bin Ren, Yanzhi Wang, “BLK-REW: A Unified Block-based Pruning Framework using Reweighted Regularization Method”, (submitted to XXXX 2020).
  • [Under review] Ning Liu*, Xiaolong Ma*, Zhengping Che, Yanzhi Wang, Jian Tang, Fachao Zhang, “Revisiting Different Pruning Schemes for DNN Model Compression”, (submitted to XXXX 2020).
  • [Under review] Zhengang Li*, Yifan Gong*, Xiaolong Ma, Sijia Liu, Mengshu Sun, Zheng Zhan, Zhenglun Kong, Geng Yuan, Yanzhi Wang, “SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency”, (submitted to XXXX 2020).
  • [Under review] Wei Niu*, Zhengang Li*, Xiaolong Ma, Peiyan Dong, Gang Zhou, Yanzhi Wang, Bin Ren, “BPDNN: A General, Real-time DNN Execution Framework on Mobile Devices with Block-based Column-Row Pruning”, (submitted to XXXX 2020).
  • [20’AAAI] Xiaolong Ma*, Fuming Guo*, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang, “PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices”, in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020, acceptance rate: 20.6%).
  • [20’AAAI] Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jieping Ye, “AutoSlim: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates”, in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020, acceptance rate: 20.6%).
  • [20’ASPLOS] Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren, “PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning”, in Proceedings of the 24th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2020, acceptance rate: 18.07%).
  • [20’DAC] Zhanhong Tan, Jiebo Song, Xiaolong Ma, Sia-Huat Tan, Hongyang Chen, Shaokai Ye, Yanzhi Wang, Kaisheng Ma, “PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators”, in Proceedings of the 57th Annual Design Automation Conference (DAC 2020).
  • [20’DAC] Chaoqun Chu, Yanzhi Wang, Yilong Zhao, Xiaolong Ma, Shaokai Ye, Yunyan Hong, Xiaoyao Liang, Yinhe Han, Yun Chen, Xiaosong Cui, and Li Jiang, “PIM-Prune: Fine-Grain DCNN pruning for Crossbar-based Process-In-Memory architecture”, in Proceedings of the 57th Annual Design Automation Conference (DAC 2020).
  • [Under review] Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shaokai Ye, Kaidi Xu, Bingbing Li, Xiaolin Xu, Sijia Liu, Qinru Qiu, Makan Fardad, Xue Lin and Caiwen Ding, “A Unified DNN Pruning Weight Framework Using Reweighted Method”, (submitted to XXXX 2020).
  • [Under review] Zheng Zhan, Yifan Gong, Zhengang Li, Wei Niu, Xiaolong Ma, Wenhao Wang, Bin Ren, Caiwen Ding, Xue Lin and Xiaolin Xu, “A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework”, (submitted to XXXX 2020).
  • [Under review] Geng Yuan, Payman Behnam, Zhengang Li, Ali Shafiei, Sheng Lin, Xiaolong Ma, Hang Liu, Xuehai Qian, Mahdi Nazm Bojnordi, Yanzhi Wang, Caiwen Ding, “FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-Signal DNN Accelerator” (submitted to XXXX 2020).
  • [20’ASP-DAC] Xiaolong Ma*, Geng Yuan*, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, Yanzhi Wang, “Tiny but Accurate: A Pruned, Quantized and Optimized Framework of an Ultra Efficient DNN Device”, in 25th Asia and South Pacific Design Automation Conference (ASP-DAC, 2020).
  • [20’ASP-DAC] Xiaolong Ma, Zhe Li, Hongjia Li, Qiyuan An, Wenyao Xu, Qinru Qiu, Yanzhi Wang. “C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing”, in in 25th Asia and South Pacific Design Automation Conference (ASP-DAC, 2020).
  • [19’ISVLSI] Ruizhe Cai, Xiaolong Ma, Olivia Chen, Ao Ren, Ning Liu, Nobuyuki Yoshikawa, Yanzhi Wang, “IDE Development, Logic Synthesis and Buffer/Splitter Insertion Framework for Adiabatic Quantum-Flux-Parametron Superconducting Circuits”, in Proceedings of the 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI, 2019).
  • [19’GLSVLSI] Hongjia Li, Ning Liu, Xiaolong Ma, Sheng Lin, Shaokai Ye, Tianyun Zhang, Xue Lin, Wenyao Xu, Yanzhi Wang, “ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices, in Proceedings of the 2019 on Great Lakes Symposium on VLSI (GLSVLSI, 2019).
  • [19’ISLPED] Geng Yuan*, Xiaolong Ma*, Caiwen Ding, Sheng Lin, Tianyun Zhang, Zeinab S. Jalali, Yilong Zhao, Li Jiang, Sucheta Soundarajan, Yanzhi Wang, “An Ultra-Efficient Memristor-Based DNN Framework with Structured Pruning and Quantization Using ADMM”, (ISLPED, 2019).
  • [19’ICESS] Zhe Li, Xiaolong Ma, Ji Li, Qinru Qiu, Yanzhi Wang, “Efficient Cloud Resource Management using Neuromorphic Modeling and Prediction for Virtual Machine Resource Utilization”, in Proceedings of the 2019 IEEE International Conference on Embedded Software and Systems (ICESS, 2019).
  • [19’NANOARCH] Xiaolong Ma, Geng Yuan, Sheng Lin, Zhengang Li, Yanzhi Wang, “ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning”, in 15th IEEE / ACM International Symposium on Nanoscale Architectures (NANOARCH, 2019).
  • [18’ASC] Olivia Chen, Xiaolong Ma, Yanzhi Wang, Naoki Takeuchi, Nobuyuki Yoshikawa, “Design and Implementation of an Extremely Energy-efficient Deep Learning Accelerator Using Superconducting Logic”, Applied Superconductivity Conference (ASC, 2018).
  • [18’AAAI] Yanzhi Wang, Caiwen Ding, Zhe Li, Geng Yuan, Siyu Liao, Xiaolong Ma, Bo Yuan, Xuehai Qian, Jian Tang, Qinru Qiu, Xue Lin. “Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework”, in AAAI Conference on Artificial Intelligence (AAAI, 2018).
  • [18’GLSVLSI] Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang. “Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs” in Proceedings of the 2018 on Great Lakes Symposium on VLSI. (GLSVLSI, 2018).
  • [17’MICRO] Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, Bo Yuan. “CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices”, in Proceedings of the International Symposium on Microarchitecture (MICRO, 2017).
  • [17’ISQED] Xiaolong Ma, Yipeng Zhang, Geng Yuan, Ao Ren, Zhe Li, Jie Han, Jingtong Hu, Yanzhi Wang. “An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing”, in International Symposium on Quality Electronic Design (ISQED, 2017). (Best Paper Nomination)
  • [17’MWSCAS] Geng Yuan, Caiwen Ding, Ruizhe Cai, Xiaolong Ma, Ziyi Zhao, Ao Ren, Bo Yuan, Yanzhi Wang. “Memristor crossbar-based ultra-efficient next-generation baseband processors”, in IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS, 2017).

Workshop publications

  • [20’BARC] Xiaolong Ma, Wei Niu, Bin Ren, Yanzhi Wang, “A Desirable Sparsity Dimension for Real-time Acceleration”, Boston Area Architecture Workshop BARC, 2020).
  • [ODML-CDNNR] Sheng Lin, Xiaolong Ma, Geng Yuan, Shaokai Ye, Kaisheng Ma, Yanzhi Wang, “Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM”, Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ICML workshop, 2019).
  • [ODML-CDNNR] Wei Niu, Xiaolong Ma, Yanzhi Wang, Bin Ren, “26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone”, Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ICML workshop, 2019).