Tang, Zhenheng

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Tang, Z., Wang, Y., Wang, Q., & Chu, X. (2019) · Proceedings of the Tenth ACM International Conference on Future Energy Systems

Parallel Computing and Optimization TechniquesEnergy Efficiency in Computing

Cited by 88

VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

Tang, Y., Dong, P., Tang, Z., Chu, X., & Liang, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Traffic Prediction and Management TechniquesAdvanced Neural Network Applications

Cited by 70

ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference

Liu, X., Tang, Z., Dong, P., Li, Z., Liu, Y., Li, B., Hu, X., & Chu, X. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs

Dong, P., Li, L., Zhong, Y., Du, D., Fan, R., Chen, Y., Tang, Z., Wang, Q., Xue, W., Guo, Y., et al. (2024) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 1