Shiyuan Huang is a PhD student in the Department of Computer Science at Shanghai Jiao Tong University (SJTU), under the supervision of Prof. Li Jiang, specializing in neural network acceleration (e.g., neural network compression ) and in-memory computing.

Publication

{=} denotes equal contribution;   {*} denotes corresponding author

TCAD-2024 Shiyuan Huang, Fangxin Liu*, Tao Yang, Zongwu Wang, Ning Yang, and Li Jiang, “SpMMPlu-Pro: An Enhanced Compiler Plug-In for Efficient SpMM and Sparsity Propagation Algorithm,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024 (CCF-A Journal).

HPCA-2025 Fangxin Liu=, Shiyuan Huang=, Ning Yang, Zongwu Wang, Haomin Li, and Li Jiang, “CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels,” 2025 IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2025 (CCF-A Conference).

TODAES-2024 Shiyuan Huang=, Fangxin Liu=, Tian Li, Zongwu Wang, Ning Yang, Haomin Li, Li Jiang, “STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization,” in ACM Transactions on Design Automation of Electronic Systems (TODAES), 2024 (CCF-B Journal).

ASP-DAC-2024 Shiyuan Huang=, Fangxin Liu=, Tian Li, Zongwu Wang, Haomin Li, and Li Jiang, “TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation,” 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024, (CCF-C Conference).

MICRO-2024 Zongwu Wang, Fangxin Liu*, Ning Yang, Shiyuan Huang, Haomin Li, and Li Jiang “COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation,” 57th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2024, (CCF-A Conference).

TPDS-2024 Fangxin Liu, Zongwu Wang, Wenbo Zhao, Ning Yang, Yongbiao Chen, Shiyuan Huang, Haomin Li, Tao Yang, Songwen Pei, Xiaoyao Liang, and Li Jiang, “Exploiting Temporal-Unrolled Parallelism for Energy-Efficient SNN Acceleration,” IEEE Transactions on Parallel and Distributed Systems (TPDS), 2024, (CCF-A Conference).

DAC-2024 Fangxin Liu=, Ning Yang=, Zhiyan Song, Zongwu Wang, Haomin Li, Shiyuan Huang, Zhuoran Song, Songwen Pei, and Li Jiang, “INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding,” 61st ACM/IEEE Design Automation Conference (DAC), 2024, (CCF-A Conference).

ASP-DAC-2025 Fangxin Liu=, Zongwu Wang=, Peng Xu, Shiyuan Huang, and Li Jiang, “Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency,” 30th Asia and South Pacific Design Automation Conference (ASP-DAC), 2025, (CCF-C Conference).

ASP-DAC-2025 Haomin Li=, Fangxin Liu=, Zewen Sun, Zongwu Wang, Shiyuan Huang, Ning Yang, and Li Jiang, “NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks,” 30th Asia and South Pacific Design Automation Conference (ASP-DAC), 2025, (CCF-C Conference).

ISLPED-2024 Fangxin Liu, Shiyuan Huang, Longyu Zhao, Li Jiang, Zongwu Wang, “LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs,” Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2024, (CCF-C Conference).