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.