Optimization of Reliability and Power Consumption in Systems on a Chip

Aggressive transistor scaling, decreased voltage margins and increased processor power and temperature, have made reliability assessment a much more significant issue in design. Although reliability of devices and interconnect has been broadly studied, here we characterize reliability at the system level. Thus we consider component-based System on Chip designs. Reliability is strongly affected by system temperature, which is in turn driven by power consumption. Thus, component reliability and their power management should be addressed jointly. We present here a joint reliability and power management optimization problem whose solution is an optimal management policy. When careful joint policy optimization is performed, we obtain a significant improvement in energy consumption (40%) in tandem with meeting reliability constraint for all operating temperatures

Randomly assembled cyclic multi-block ADMM: a fast method for large-scale linearly constrained quadratic optimization

This work is motivated by a simple question: how to find a relatively good solution to a very large optimization problem in a limited amount of time. We consider the linearly constrained convex minimization model with an objective function that is the sum of multiple separable functions and a coupled quadratic function. Such problems naturally arise from applications such as machine and statistical learning, image processing, portfolio management, tensor decomposition, matrix completion or decomposition, manifold optimization, data clustering and many other problems of practical importance. This thesis focuses on the development of new algorithms that are based on the alternating direction method of multipliers (ADMM). The first part proposes a randomly assembled multi-block and cyclic alternating direction method of multipliers (RAC-ADMM), a novel ADMM algorithm with which we hope to mitigate both the problem of slow convergence and the issues with divergence that arise when the multi-block ADMM is applied to convex problems with cross-block coupled variables. We discuss the theoretical properties of RAC-ADMM and show when random assembling helps and when it hurts; allowing us to develop criteria for almost sure convergence. The second part utilizes the aforementioned theoretical guidance on RAC-ADMM to investigate solution methods for continuous and mixed integer quadratic problems (QPs, MIQPs). First, we develop a robust and efficient general-purpose QP solver, RACQP, capable of detecting infeasible and unbounded problems. Next, we devise two extensions that enable RACQP to address MIQP – one applicable to situations where a solution needs to be found rapidly but some level of primal infeasibility can be tolerated; and the other that implements a custom branch-and-bound algorithm. Multiple numerical tests, conducted on both randomly-generated and large-scale benchmark instances show that, for most quadratic optimization problems, RACQP matches, and often exceeds, the computational efficiency of commercial solvers and competing ADMM-based solvers

A system for online power prediction in virtualized environments using Gaussian mixture models

In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implemen-tation of our system shows that it can achieve average prediction error of less than 10%, outperforming state of the art regression based approaches at negligible runtime overhead