A power modeling and characterization method for macrocells using structure information

Authors:
Jing-Yuan Lin;Wen-Zen Shen;Jing-Yang Jou
Affiliations:
Dept. of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu 30050, Taiwan R.O.C;Dept. of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu 30050, Taiwan R.O.C;Dept. of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu 30050, Taiwan R.O.C
Venue:
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Year:
1997

Citing 7
Cited 3

A cell-based power estimation in CMOS combinational circuits

ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Architectural power analysis: the dual bit type method

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
HEAT: hierarchical energy analysis tool

DAC '96 Proceedings of the 33rd annual Design Automation Conference
Energy characterization based on clustering

DAC '96 Proceedings of the 33rd annual Design Automation Conference
Register-transfer level estimation techniques for switching activity and power consumption

Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Instruction level power profiling

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Activity-sensitive architectural power analysis

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

The design and use of simplepower: a cycle-accurate energy estimation tool

Proceedings of the 37th Annual Design Automation Conference
Speeding up power estimation of embedded software

ISLPED '00 Proceedings of the 2000 international symposium on Low power electronics and design
Evaluating Integrated Hardware-Software Optimizations Using a Unified Energy Estimation Framework

IEEE Transactions on Computers

Quantified Score

Hi-index	0.01

Visualization

Abstract

To characterize a macrocell, a general method is to store the power consumption of all possible transition events at primary inputs in the lookup tables. Though this approach is very accurate, the lookup tables could be huge for the macrocells with many inputs. In this paper, we present a new power modeling method which takes advantage of the structure information of macrocells and selects minimum number of primary inputs or internal nodes in a macrocell as state variables to build a state transition graph (STG). Those state variables can completely model the transitions of all internal nodes and the primary outputs. By carefully deleting some state variables, we further introduce an incomplete power modeling technique which can simplify the STG without losing much accuracy. In addition, we exploit the property of the compatible patterns of a macrocell to further reduce the number of edges in the corresponding STG. Experimental results show that our modeling techniques can provide SPICE-like accuracy and can reduce the size of the lookup table significantly comparing to the general approach.