Systematic Design Principles for Cost-Effective Hard Constraint Management in Dynamic Nonlinear Systems

Abstract

Modern cost-conscious dynamic systems incorporate knobs that allow run-time trade-offs between system metrics of interest. In these systems regular knob tuning to minimize costs while satisfying hard system constraints is an important aspect. Knob tuning is a combinatorial constrained nonlinear dynamic optimization problem with uncertainties and time-linkage. Hiding uncertainties under worst-case bounds, reacting after the fact, optimizing only the present, and applying static greedy heuristics are widely used problem simplification strategies to keep the design complexity and decision overhead low. Applying any of these will result in highly sub-optimal system realizations in the presence of nonlinearities. The more recently introduced System Scenarios methodology can only handle limited form of dynamics and nonlinearities. Existing predictive optimization approaches are far from optimal as they do not fully exploit the predictability of the system at hand. To bridge this gap, the authors propose the combined strategy of dynamic bounding and proactive system conditioning for the predicted likely future. This paper describes systematic principles to design low-overhead controllers for cost-effective hard constraint management. When applied to fine-grain performance scaling mode assignment problem in a video decoder design, proposed concepts resulted in more than 2x energy gains compared to state-of-the-art techniques.