Date of Award

Summer 7-2-2014

Document Type


Degree Name

Doctor of Philosophy (PhD)


Electrical, Computer & Energy Engineering

First Advisor

Sriram Sankaranarayanan

Second Advisor

Fabio Somenzi

Third Advisor

Dragan Maksimovic


This thesis discusses two important problems for the design of practical systems under stochastic parameter variations: verification and optimization. Verification is concerned with the safety of a system, i.e., whether a system satisfies its specifications. If not, optimization is applied to tune the design parameters in the system so that the new design is safe. This thesis treats systems as black-boxes, assuming that the systems can be simulated efficiently but without detailed knowledge of the internal workings. It presents a series of simulation-based techniques to solve the problems of design verification and optimization. A notion called statistical soundness is introduced in this thesis, which guarantees that the outcome of the proposed techniques are “statistically certified” in the sense that the probability of drawing a wrong conclusion is bounded. For the problem of verification, this thesis develops a statistically sound model inference (SSMI) approach. SSMI constructs statistically sound models to explain the relationship between the stochastic parameters and the responses of a system. To improve the scalability of SSMI, a sparse approximation algorithm is also introduced. For the problem of optimization, this thesis presents a statistically sound optimization technique, SSMI-opt. SSMI-opt aims to find values of the design parameters for which the system satisfies the specifications. The proposed techniques can be applied to many interesting areas, including analog/mixd-signal circuits, embedded systems, biological systems, and medical devices. This thesis demonstrates the utility of this methodology on several interesting benchmark examples.