Statistical on-chip interconnect modeling : an application of automatic differentiation.

Conference · Mon Sep 28 04:00:00 EDT 1998

OSTI ID:775251

Bischof, C; Roh, L; Chang, N; Lee, K; Kanevsky, V; Nakagawa, O S; Oh, S -Y

Automatic differentiation is a technique for computing derivatives accurately and efficiently with minimal human effort. We employed this technique to generate derivative information of FCAP2 (2-D) and FCAP3 (3-D) programs that simulate the parasitic effects of interconnects and devices. This derivative information is used in the statistical modeling of worst-case interconnect delays and on-chip crosstalks. The ADIC (Automatic Differentiation in C) tool generated new versions of FCAP2 and FCAP3 programs that compute both the original results and the derivative information. Given the ANSI C source code for the function, ADIC generates new code that computes derivatives of the model output with respect to the input parameters. We report on the use of automatic differentiation and divided difference approaches for computing derivatives for FCAP3 programs. The results show that ADIC-generated code computes derivatives more accurately, more robustly, and faster than the divided difference approach.

• Research Organization: Argonne National Lab., IL (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: W-31109-ENG-38
• OSTI ID: 775251
• Report Number(s): ANL/MCS-P698-1097
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ANL
AUTOMATION
DESIGN