A survey of power estimation techniques in VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
ISLPED '96 Proceedings of the 1996 international symposium on Low power electronics and design
Cycle-accurate macro-models for RT-level power analysis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Dynamic power estimation using the probabilistic contribution measure (PCM)
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Power modeling for high-level power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Analytical macromodeling for high-level power estimation
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
Improving the efficiency of Monte Carlo power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on the 11th international symposium on system-level synthesis and design (ISSS'98)
Accurate Entropy Calculation for Large Logic Circuits Based on Output Clustering
GLS '97 Proceedings of the 7th Great Lakes Symposium on VLSI
ESL power analysis of embedded processors for temperature and reliability estimations
CODES+ISSS '09 Proceedings of the 7th IEEE/ACM international conference on Hardware/software codesign and system synthesis
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This paper describes LP-DSM, which is an algorithm used for efficient library characterization in high-level power estimation. LP-DSM characterizes the power consumption of building blocks using the entropy of primary inputs and primary outputs. The experimental results showed that over a wide range of benchmark circuits implemented using full custom design in 0.35-µm 3.3 V CMOS process the statistical performance (mean and maximum error) of LP-DSM is comparable or sometimes better than most of the published algorithms. Moreover, it was found that LP-DSM has the lowest prediction sum of squares, which makes it an efficient tool for power prediction. Furthermore, the complexity of the LP-DSM is linear in relation to the number of primary inputs (O(NI)), whereas state of the art published library characterization algorithms have a complexity of O(NI2).