Song Han

Song Han is an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). His research focuses on efficient deep learning computing. He has proposed “deep compression” as a way to reduce neural network size by an order of magnitude, and the hardware implementation “efficient inference engine” that first exploited model compression and weight sparsity in deep learning accelerators. He has received best paper awards at the International Conference on Learning Representations and Field-Programmable Gate Arrays symposium. He is also a recipient of an NSF Career Award and MIT Tech Review’s 35 Innovators Under 35 award. Many of his pruning, compression, and acceleration techniques have been integrated into commercial artificial intelligence chips. He earned a PhD in electrical engineering from Stanford University.

Selected Publications

• Yang, S., Guo, J., Tang, H., Hu, Q., Xiao, G., Tang, J., Lin, Y., Liu, Z., Lu, Y., & Han, S. (2025). LServe: Efficient long-sequence LLM serving with unified sparse attention. Proceedings of the 8th MLSys Conference.
• Lin, Y., Zhang, Z., & Han, S. (2025). LEGO: Spatial accelerator generation and optimization for tensor applications. IEEE International Symposium on High Performance Computer Architecture (HPCA) (pp. 1335–1347).
• Li, M., Lin, Y., Zhang, Z., Cai, T., Li, X., Guo, J., Xie, E., Meng, C., Zhu, J.-Y., & Han, S. (2025). SVDQuant: Absorbing outliers by low-rank components for 4-bit diffusion models. In Proceedings of the International Conference on Learning Representations (ICLR).

Media

• June 24, 2025: MIT News, The tenured engineers of 2025
• March 21, 2025: MIT News, AI tool generates high-quality images faster than state-of-the-art approaches
• February 13, 2024: MIT News, A new way to let AI chatbots converse all day without crashing