Three papers from our lab have been published recently.

The first paper/work  exploits the differential natural of DNN networks to significantly speedup the gradient based optimization method for power grid networks subject to electromigration constraints.  This work was led by Ph.D. student Han Zhou and it also was  nominated as the Best Paper Award.

• H. Zhou, W. Jin, and S. X.-D. Tan, “GridNet: Fast date-driven EM-induced IR drop prediction and localized fixing for on-chip power grid networks”, Proc. IEEE/ACM International Conf. on Computer-Aided Design (ICCAD’18), San Diego, CA,  Nov.  2020. (23.8% acceptance rate) (nominated as Best Paper Awards)

The second works led by Ph.D. student Amir Kavousi and post-doc Liang Chen developed a new electromigration immortality check method considering the self-heating (Joule heating) effects for the first time. 

• M. Kavousi, L. Chen, and S. X.-D. Tan, “Electromigration immortality check considering Joule heating effect of multi-segment wires”, Proc. IEEE/ACM International Conf. on Computer-Aided Design (ICCAD’18), San Diego, CA,  Nov.  2020. (23.8% acceptance rate)

The third work developed a GAN-based machine learning method for full-chip thermal analysis of multi-core commercial CPUs for the first time. 

• W. Jin, S. Sadiqbatcha, J. Zhang and S. X.-D. Tan, “Full-chip thermal map estimation for multi-core commercial CPUs with generative adversarial learning”, Proc. IEEE/ACM International Conf. on Computer-Aided Design (ICCAD’18), San Diego, CA,  Nov.  2020. (invited)