Parallel statistical analysis of analog circuits by GPU-accelerated graph-based approach

• Published in: Proceedings of the Conference on Design, Automation and Test in Europe pp. 852 - 857
• Main Authors: Liu, Xue-Xin, Tan, Sheldon X.-D., Wang, Hai
• Format: Conference Proceeding
• Language: English
• Published: San Jose, CA, USA EDA Consortium 12.03.2012
• Series: ACM Conferences
• Subjects: Computing methodologies > Computer graphics > Graphics systems and interfaces > Graphics processors; Hardware > Electronic design automation > Physical design (EDA); Hardware > Emerging technologies; Hardware > Very large scale integration design
• ISBN: 3981080181; 9783981080186
• DOI: 10.5555/2492708.2492920

In this paper, we propose a new parallel statistical analysis method for large analog circuits using determinant decision diagram (DDD) based graph technique based on GPU platforms. DDD-based symbolic analysis technique enables exact symbolic analysis of vary large analog circuits. But we show that DDD-based graph analysis is very amenable for massively threaded based parallel computing based on GPU platforms. We design novel data structures to represent the DDD graphs in the GPUs to enable fast memory access of massive parallel threads for computing the numerical values of DDD graphs. The new method is inspired by inherent data parallelism and simple data independence in the DDD-based numerical evaluation process. Experimental results show that the new evaluation algorithm can achieve about one to two order of magnitudes speedup over the serial CPU based evaluations and 2--3 times speedup over numerical SPICE-based simulation method on some large analog circuits.