lengcong@airia.cn
北京市海淀区中关村东路95号
机器学习、深度学习、人工智能芯片设计、视觉大模型、模型量化加速
冷聪,中科方寸知微(南京)科技有限公司总经理。2011年获得中南大学学士学位,2016年获得中国科学院自动化研究所博士学位。中国科学院自动化研究所模式识别国家重点实验室副研究员,中国科学院院长奖获得者。主要从事机器学习、深度学习、人工智能芯片设计、视觉大模型、模型量化加速等方面的研究。
[1] C Leng, J Wu, J Cheng, X Zhang, H Lu. Hashing for distributed data. International Conference on Machine Learning (ICML), 2015.
[2] Cong Leng, Jiaxiang Wu, Jian Cheng, Xiao Bai, Hanqing Lu. Online Sketching Hashing. CVPR 2015.[code]
[3] Qiang Song, Sixie Yu, Cong Leng, Jiaxiang Wu, Qinghao Hu, Jian Cheng. Learning Deep Features for MSR-Bing Information Retrieval Challenge. ACM Multimedia 2015. (Ranked the 1st place on Visual Recognition task and 3rd place on Image Retrieval task)
[4] J Wu, C Leng, Y Wang, Q Hu, J Cheng. Quantized convolutional neural networks for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[5] Jiaxiang Wu, Qinghao Hu, Cong Leng, Jian Cheng. Shoot to Know What: An Application of Deep Networks on Mobile Devices. AAAI 2016 (Demo track).
[6] Jian Cheng, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu. Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Vol.29, No.10, pp.4730-4743, 2018.
[7] C Leng, Z Dou, H Li, S Zhu, R Jin. Extremely low bit neural network: Squeeze the last bit out with admm Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018
[8] Fanrong Li, Zitao Mo, Peisong Wang, Zejian Liu, Jiayun Zhang, Gang Li, Qinghao Hu, Xiangyu He, Cong Leng, Yang Zhang, Jian Cheng. A System-Level Solution for Low-Power Object Detection. ICCV 2019 Workshop on Low-Power Computer Vision.
[9] Gang Li, Peisong Wang, Zejian Liu, Cong Leng, Jian Cheng. Hardware Acceleration of CNN with One-Hot Quantization of Weights and Activations. DATE 2020.
[10] Tianli Zhao, Qinghao Hu, Xiangyu He, Weixiang Xu, Jiaxing Wang, Cong Leng, Jian Cheng. ECBC: Efficient Convolution via Blocked Columnizing. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press,2022.