Wavelet analysis for microprocessor design: experiences with wavelet-based dI/dt characterization

As microprocessors become increasingly complex, the techniques used to analyze and predict their behavior must become increasingly rigorous. We apply wavelet analysis techniques to the problem of dl/dt estimation and control in modern microprocessors. While prior work has considered Bayesian phase analysis, Markov analysis, and other techniques to characterize hardware and software behavior, we know of no prior work using wavelets for characterizing computer systems. The dl/dt problem has been increasingly vexing in recent years, because of aggressive drops in supply voltage and increasingly large relative fluctuations in CPU current dissipation. Because the dl/dt problem has natural frequency dependence (it is worst in the mid-frequency range of roughly 50-200 MHz) it is natural to apply frequency-oriented techniques like wavelets to understand it. Our work proposes (i) an offline wavelet-based estimation technique that can accurately predict a benchmark's likelihood of causing voltage emergencies, and (ii) an online wavelet-based control technique that uses key wavelet coefficients to predict and avert impending voltage emergencies. The offline estimation technique works with roughly 0.94% error. The online control technique reduces false positives in dl/dt prediction, allowing, voltage control to occur with less than 2.5% performance overhead on the SPEC benchmark suite.