OminiX – Unified Acceleration Framework for Both LLM and SD GenAI Models on the
Yanzhi Wang is currently an associate professor and faculty fellow at Dept. of ECE at Northeastern University, Boston, MA. He received the B.S. degree from Tsinghua University in 2009, and Ph.D. degree from University of Southern California in 2014. His research interests focus on model compression and platform-specific acceleration of deep learning applications. His work has been published broadly in top conference and journal venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, PLDI, ICS, PACT, ISSCC, AAAI, ICML, NeurIPS, CVPR, ICLR, IJCAI, ECCV, ICDM, ACM MM, FPGA, LCTES, CCS, VLDB, PACT, ICDCS, RTAS, Infocom, C-ACM, JSSC, TComputer, TCAS-I, TCAD, TCAS-I, JSAC, TNNLS, etc.), and has been cited above 18,000 times. He has received six Best Paper and Top Paper Awards, and one Communications of the ACM cover featured article. He has another 13 Best Paper Nominations and four Popular Paper Awards. He has received the U.S. Army Young Investigator Program Award (YIP), IEEE TC-SDM Early Career Award, APSIPA Distinguished Leader Award, Massachusetts Acorn Innovation Award, Martin Essigmann Excellence in Teaching Award, Massachusetts Acorn Innovation Award, Ming Hsieh Scholar Award, and other research awards from Google, MathWorks, etc. He has received 26 federal grants from NSF, DARPA, IARPA, ARO, ARFL/AFOSR, Dept. of Homeland Security, etc.. He has participated in a total of $40M funds with personal share $8.5M. 14 of his academic descendants become tenure track faculty at Univ. of Connecticut, Clemson University, Chongqing University, University of Georgia, Villanova University University of Texas San Antonio, and Cleveland State University.