CV

Personal Information

Name: Ning Yang

Phone: 15201952982

Email: yn937391832@sjtu.edu.cn

Education

Ph.D in computer science and technology: Shanghai Jiao Tong University (SJTU), 2021.09 – Present
Intelligent Memory & Processor Architecture & Computing Lab, Advisor: Li Jiang

B.S. in computer science and technology: Shanghai Jiao Tong University (SJTU), 2017.09 – 2021.06
Zhiyuan Honors Program

Research Interests

• Model Quantization & Compression
• Memory-efficient execution and dataflow optimization
• Mixture-of-Experts (MoE) inference and scheduling
• Hardware–software co-design for LLM deployment

Publications

NICE: Deep Neural Network Acceleration via Hardware-Friendly Index Assisted Compression
Ning Yang, Fangxin Liu, Zongwu Wang, Haomin Li, Hongbo Zhao, Xinran Liang, Li Jiang, Haibing Guan
TACO 2026 (CCF-A)

Rethinking variable-length encoding: Exploiting bit sparsity for parallel decoding in LLM accelerators
Ning Yang, Fangxin Liu, Junjie Wang, Chenyang Guan, Zongwu Wang, Junping Zhao, Li Jiang, Haibing Guan
TACO 2026 (CCF-A)

PISA: Efficient Precision-Slice Framework for LLMs with Adaptive Numerical Type
Ning Yang, Zongwu Wang, Qingxiao Sun, Liqiang Lu, Fangxin Liu
DAC 2025 (CCF-A)

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies
Ning Yang, Fangxin Liu, Junjie Wang, Tao Yang, Kan Liu, Haibing Guan, Li Jiang
arXiv 2025 (Preprint)

Searchq: Search-based fine-grained quantization for data-free model compression
Ning Yang, Fangxin Liu, Zongwu Wang, Junping Zhao, Li Jiang
TCAS-AI 2024

STEP: Adaptive Spatio-Temporal Expert Prefetching for Low-Latency and Memory-Efficient MoE Inference (Accepted!)
Fangxin Liu=, Ning Yang=, Zongwu Wang, Chenyang Guan, Haomin Li, Yu Feng, Liqiang Lu, Xiang Li, Siran Yang, Jiamang Wang, Lin Qu, Li Jiang, Haibing Guan
ISCA 2026 (CCF-A)

EARTH: An Efficient MoE Accelerator with Entropy-Aware Speculative Prefetch and Result Reuse
Fangxin Liu=, Ning Yang=, Jingkui Yang, Zongwu Wang, Chenyang Guan, Yu Feng, Li Jiang, Haibing Guan
ASPLOS 2026 (CCF-A)

BLOOM: Bit-Slice Framework for DNN Acceleration with Mixed-Precision
Fangxin Liu=, Ning Yang=, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Li Jiang, Haibing Guan
DAC 2025 (CCF-A)

Ops: Outlier-aware precision-slice framework for llm acceleration
Fangxin Liu=, Ning Yang=, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Qi Sun, Li Jiang
DATE 2025 (CCF-B)

EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks
Ning Yang=, Fangxin Liu=, Zongwu Wang, Haomin Li, Zhuoran Song, Songwen Pei, Li Jiang
DAC 2024 (CCF-A)

Inspire: Accelerating deep neural networks via hardware-friendly index-pair encoding
Fangxin Liu=, Ning Yang=, Zhiyan Song, Zongwu Wang, Haomin Li, Shiyuan Huang, Zhuoran Song, Songwen Pei, Li Jiang
DAC 2024 (CCF-A)

Spark: Scalable and precision-aware acceleration of neural networks via efficient encoding
Fangxin Liu=, Ning Yang=, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang
HPCA 2024 (CCF-A)

T-BUS: Taming bipartite unstructured sparsity for energy-efficient DNN acceleration
Ning Yang=, Fangxin Liu=, Zongwu Wang, Zhiyan Song, Tao Yang, Li Jiang
ICCD 2024 (CCF-B)

Holes: Boosting large language models efficiency with hardware-friendly lossless encoding
Fangxin Liu=, Ning Yang=, Zhiyan Song, Zongwu Wang, Li Jiang
ICCD 2024 (CCF-B)

Aster: Adaptive dynamic layer-skipping for efficient transformer inference via markov decision process
Fangxin Liu, Junjie Wang, Ning Yang, Zongwu Wang, Junping Zhao, Li Jiang, Haibing Guan
ACM MM 2025 (CCF-A)

TAIL: Exploiting temporal asynchronous execution for efficient spiking neural networks with inter-layer parallelism
Haomin Li, Fangxin Liu, Zongwu Wang, Dongxu Lyu, Shiyuan Huang, Ning Yang, Qi Sun, Zhuoran Song, Li Jiang
DATE 2025 (CCF-B)

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation
Fangxin Liu, Ning Yang, Junping Zhao, Tao Yang, Haibing Guan, Li Jiang
arXiv 2025 (Preprint)

Irregular Sparsity-Enabled Search-in-Memory Engine for Accelerating Spiking Neural Networks
Fangxin Liu, Zongwu Wang, Ning Yang, Haomin Li, Tao Yang, Haibing Guan, Li Jiang
APPT 2025

Paap-hd: Pim-assisted approximation for efficient hyper-dimensional computing
Fangxin Liu, Haomin Li, Ning Yang, Yichi Chen, Zongwu Wang, Tao Yang, Li Jiang
ASPDAC 2024 (CCF-B)

Teas: Exploiting spiking activity for temporal-wise adaptive spiking neural networks
Fangxin Liu, Haomin Li, Ning Yang, Zongwu Wang, Tao Yang, Li Jiang
ASPDAC 2024 (CCF-B)

Compass: Sram-based computing-in-memory snn accelerator with adaptive spike speculation
Zongwu Wang, Fangxin Liu, Ning Yang, Shiyuan Huang, Haomin Li, Li Jiang
MICRO 2024 (CCF-A)

Exploiting temporal-unrolled parallelism for energy-efficient snn acceleration
Fangxin Liu, Zongwu Wang, Wenbo Zhao, Ning Yang, Yongbiao Chen, Shiyuan Huang, Haomin Li, Tao Yang, Songwen Pei, Xiaoyao Liang, others
TPDS 2024 (CCF-A)

STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization
Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Ning Yang, Haomin Li, Li Jiang
TODAES 2024 (CCF-B)

SpMMPlu-Pro: An enhanced compiler plug-in for efficient SpMM and sparsity propagation algorithm
Shiyuan Huang, Fangxin Liu, Tao Yang, Zongwu Wang, Ning Yang, Li Jiang
TCAD 2024 (CCF-A)

Attack and Defense: Enhancing Robustness of Binary Hyper-Dimensional Computing
Haomin Li, Fangxin Liu, Zongwu Wang, Ning Yang, Shiyuan Huang, Xiaoyao Liang, Haibing Guan, Li Jiang
TACO 2024 (CCF-A)

PSQ: An automatic search framework for data-free quantization on pim-based architecture
Fangxin Liu, Ning Yang, Li Jiang
ICCD 2023 (CCF-B)

Projects & Experiences

• Xiaomi: On-Device Large Model Low-Bit Quantization

• Huawei: Optimization Scheme Analysis of Cold/Hot Aware Scheduling Based on "Query instead of Calculation"

• ZTE: High-Efficiency Quantization & Compression Platform for Multi-Model Adaptation

• Huawei: Low-Power Optical Communication Operators Based on Computing-in-Memory

Awards & Honors

• National Scholarship, 2024

• Best Paper Poster Award, 3rd CCF China Computer Systems Conference (CCS), 2024

• First Prize, 2nd Open Source Competition for Integrated Chips & Chiplet Technology, 2024

Academic Services

Reviewer for DAC, TACO, TC, ACL, etc.