个人简介 :

莫子韬，硕士毕业于中国科学院自动化研究所，主要从事深度学习算法、神经网络压缩与加速、软硬件协同设计等方面的研究，曾在ICLR、NeurIPS等国际会议上发表论文，相信大模型在通用人工智能中的核心作用。退役虚无主义者，认同科学主义的非还原论者。

发表论文 :

• Xiangyu H, Zitao M, Ke C, Weixiang X, Qinghao H, Peisong W, Qingshan L, Jian C, et al. ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices[C], European Conference on Computer Vision, 2020: 223-241.
• Fanrong L, Gang L, Zitao M, Xiangyu H, Jian C, et al. FSA: A Fine-Grained Systolic Accelerator for Sparse CNNs[J], IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(11): 3589-3600.
• Xingting Y, Fanrong L, Zitao M, Jian C, et al. GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks[C], Conference on Neural Information Processing Systems, 2022 
• Zeyu Z, Fanrong L, Zitao M, Qinghao H, Gang L, Zejian L, Xiaoyao L, Jian C, et al. : Aggregation-Aware Quantization for Graph Neural Networks[J], ICLR 2023, 2023
• Zeyu Z, Fanrong L, Gang L, Zejian L, Zitao Mo, Qinghao H, Xiaoyao L, Jian C. MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization. HPCA 2024.