Reducing power while increasing performance with supercisc
ACM Transactions on Embedded Computing Systems (TECS)
A multi-model power estimation engine for accuracy optimization
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
A design automation and power estimation flow for RFID systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Interconnect customization for a hardware fabric
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Decomposition-based vectorless toggle rate computation for FPGA circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A multi-model engine for high-level power estimation accuracy optimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Power estimation of CMOS circuits by neural network macromodel
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Sequential algorithm for low-power encoding internal states of finite state machines
Journal of Computer and Systems Sciences International
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In this paper, we present a novel sequence generator based on a Markov chain (MC) model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl+n2) time. Derived from a strongly mixing MC, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2000 48-bit vectors are generated in less than 0.05 s, with average deviations of the signal statistics p,d, and s equal to 1.6%, 1.8%, and 2.8%, respectively. Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS'85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p,d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.