An efficient algorithm for statistical minimization of total power under timing yield constraints

TLDR

Power minimization under variability is formulated as a rigorous statistical robust optimization program that treats both power and timing metrics probabilistically, guaranteeing power and timing yields. The study aims to reduce power by simultaneously sizing cells and assigning dual threshold voltages. The algorithm casts the problem as a second‑order conic program and solves it with efficient interior‑point methods, yielding very fast run‑time. Compared to deterministic optimization, the algorithm cuts static power by 31 % and total power by 17 % without sacrificing parametric yield, and it runs 30× faster than other statistical power‑minimization methods on public and industrial benchmarks.

Abstract

Power minimization under variability is formulated as a rigorous statistical robust optimization program with a guarantee of power and timing yields. Both power and timing metrics are treated probabilistically. Power reduction is performed by simultaneous sizing and dual threshold voltage assignment. An extremely fast run-time is achieved by casting the problem as a second-order conic problem and solving it using efficient interior-point optimization methods. When compared to the deterministic optimization, the new algorithm, on average, reduces static power by 31% and total power by 17% without the loss of parametric yield. The run time on a variety of public and industrial benchmarks is 30X faster than other known statistical power minimization algorithms.