Xiaolong Ma
Xiaolong Ma
Assistant Professor, Clemson University
Verified email at - Homepage
Cited by
Cited by
CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices
C Ding, S Liao, Y Wang, Z Li, N Liu, Y Zhuo, C Wang, X Qian, Y Bai, ...
Proceedings of the 50th Annual IEEE/ACM International Symposium on…, 2017
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
W Niu, X Ma, S Lin, S Wang, X Qian, X Lin, Y Wang, B Ren
Proceedings of the Twenty-Fifth International Conference on Architectural…, 2020
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.
N Liu, X Ma, Z Xu, Y Wang, J Tang, J Ye
AAAI, 4876-4883, 2020
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices.
X Ma, FM Guo, W Niu, X Lin, J Tang, K Ma, B Ren, Y Wang
AAAI, 5117-5124, 2020
StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs
T Zhang, S Ye, X Feng, X Ma, K Zhang, Z Li, J Tang, S Liu, X Lin, Y Liu, ...
IEEE Transactions on Neural Networks and Learning Systems, 2021
SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning
Z Kong, P Dong, X Ma, X Meng, W Niu, M Sun, X Shen, G Yuan, B Ren, ...
European Conference on Computer Vision (ECCV), 620-640, 2022
Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?
X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, SH Tan, Z Li, D Fan, X Qian, ...
IEEE transactions on neural networks and learning systems, 2021
Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm
S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ...
arXiv preprint arXiv:1903.09769, 2019
VAQF: fully automatic software-hardware co-design framework for low-bit vision transformer
M Sun, H Ma, G Kang, Y Jiang, T Chen, X Ma, Z Wang, Y Wang
arXiv preprint arXiv:2201.06618, 2022
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
G Yuan, P Behnam, Z Li, A Shafiee, S Lin, X Ma, H Liu, X Qian, ...
Proceedings of the 48th International Symposium on Computer Architecture…, 2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ...
Advances in Neural Information Processing Systems (NeurIPS), 2021
Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation
X Ma, G Yuan, S Lin, C Ding, F Yu, T Liu, W Wen, X Chen, Y Wang
2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), 301-306, 2020
CHEX: CHannel EXploration for CNN Model Compression
Z Hou, M Qin, F Sun, X Ma, K Yuan, Y Xu, YK Chen, R Jin, Y Xie, SY Kung
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12287…, 2022
An ultra-efficient memristor-based DNN framework with structured weight pruning and quantization using ADMM
G Yuan, X Ma, C Ding, S Lin, T Zhang, ZS Jalali, Y Zhao, L Jiang, ...
2019 IEEE/ACM International Symposium on Low Power Electronics and Design…, 2019
PIM-prune: Fine-grain DCNN pruning for crossbar-based process-in-memory architecture
C Chu, Y Wang, Y Zhao, X Ma, S Ye, Y Hong, X Liang, Y Han, L Jiang
2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020
Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework
Y Wang, C Ding, Z Li, G Yuan, S Liao, X Ma, B Yuan, X Qian, J Tang, ...
Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018
Film-qnn: Efficient fpga acceleration of deep neural networks with intra-layer, mixed-precision quantization
M Sun, Z Li, A Lu, Y Li, SE Chang, X Ma, X Lin, Z Fang
Proceedings of the 2022 ACM/SIGDA International Symposium on Field…, 2022
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
G Yuan, X Ma, W Niu, Z Li, Z Kong, N Liu, Y Gong, Z Zhan, C He, Q Jin, ...
Advances in Neural Information Processing Systems (NeurIPS), 2021
ResNet Can Be Pruned 60: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
X Ma, G Yuan, S Lin, Z Li, H Sun, Y Wang
2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 1-2, 2019
Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, et al. Mest: Accurate and fast memory-economic sparse training framework on the edge
G Yuan, X Ma, W Niu, Z Li
Advances in Neural Information Processing Systems 34 (20838-20850), 2-4, 2021
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