CADathlon 2019 Problem References

Problem 1: Circuit Design and Analysis
Contributed by Jianlei Yang, Beihang University
Overview: Solve Landau-Lifshitz-Gilbert (LLG) equation (in C++)
Reference: Iwasaki, Junichi, Masahito Mochizuki, and Naoto Nagaosa. “Current-induced skyrmion dynamics in constricted geometries,” Nature nanotechnology 8.10 (2013): 742.

Problem 2: Physical Design & Design for Manufacturability
Contributed by William Chow, Cadence
Overview: Tap assignment for gated clock network (in C++)
Reference: W-H Chen, C-K Wang, H-M Chen, Y-C Chou, and C-H Tsai, “A Comparative Study on Multisource Clock Network Synthesis,” The 22nd Workshop on Synthesis And System Integration of Mixed Information technologies (SASIMI), 2016

Problem 3: Logic & High-Level Synthesis
Overview: Boolean Function Manipulation by Quantification (in C++)
Reference: No specific reference is provided.

Problem 4: System Design & Analysis
Contributed by Andy Yu-Guang Chen, National Central University
Overview: On-line Wake-up Scheduling for Multi-module design (in C++)
Reference 1: D. Brelaz, “New Methods to Color the Vertices of a Graph,” Communications of the ACM, Vol.22, Issue 4, Apr. 1979.
Reference 2: M.C. Lee, Y. Shi, Y.G. Chen, D. Marculescu, S.C. Chang, “Efficient On-Line Module-Level Wake-Up Scheduling for High Performance Multi-Module Designs,” Proc. on the International Symposium on Physical Design (ISPD), 2012, Page(s): 97-104.

Problem 5: Functional Verification & Testing
Contributed by Hao Zheng, University of South Florida
Overview: Cycle-based logic simulation (in C++)
Reference 1: S. Palnitkar and D. Parham, “Cycle Simulation Techniques,” IEEE International Verilog HDL Conference, 1995, Page(s) 2-8.
Reference 2: A. Biere, “The AIGER And-Inverter Graph (AIG) Format, Version 20070427,” Johannes Kepler University, 2006-2007

Problem 6: Future technologies (Bio-EDA, Security, AI, etc.)
Contributed by Mimi Xie, The University of Texas at San Antonio and Caiwen Ding, University of Connecticut
Overview: Efficient Pruning for Neural Networks (in Python)
Reference: Han, Song, Jeff Pool, John Tran, and William Dally. “Learning both weights and connections for efficient neural network,” In Advances in neural information processing systems, pp. 1135-1143. 2015.