Lp. Yuan et Sm. Kang, AVERAGE POWER ANALYSIS OF SEQUENTIAL-CIRCUITS USING AN AUTOREGRESSIVEMODEL, Circuits, systems, and signal processing, 17(2), 1998, pp. 289-304
We present a new statistical technique for average power estimation in
sequential circuits. Because of the feedback loops, power dissipation
s of sequential circuits in consecutive clock cycles are temporally co
rrelated. The existence of data correlation makes it unsuitable to app
ly conventional techniques to average power inference, because the sam
ple variance is no longer a maximum likelihood estimator. The converge
nce criterion derived from the biased variance estimation will be over
ly optimistic, causing power simulation to stop prematurely at a lower
-than-specified estimation accuracy. To overcome this problem, we prop
ose a systematic approach for modeling the power dissipation behavior
of sequential circuits as an autoregressive random process. An accurat
e process variance can be obtained by the model parameters, which enab
les the derivation of a robust confidence interval of the average powe
r. The interval is checked for convergence against a user-specified ac
curacy criterion. An iterative procedure is developed to invoke these
steps repeatedly until the convergence specification is met. For a set
of benchmark sequential circuits, this technique yields high accuracy
and efficiency.