CSR: Medium: Optimal Control of Approximate Computing Systems

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Computing is increasingly constrained by energy both at the low end in portable devices like cell phones and at the high end in large-scale data centers. Therefore, reducing the energy expended in computation is one of the most important problems facing Computer Science today. By taking computational shortcuts such as not executing certain portions of the program or executing them with lower accuracy, it is possible in many applications, for instance video processing, to reduce energy requirements without substantially affecting the quality of the output. The goal of the Capri project is to build a system that can optimize the energy consumption of a program in these ways while guaranteeing that the output quality stays within some bound specified by the programmer.There are two major problems that must be solved: (i) building models to characterize the output quality and energy behavior of a program, and (ii) using these models to solve a constrained optimization problem to determine how approximation can be used. Capri will use state-of-the-art machine learning techniques to build program models for output quality and energy behavior, and will employ modern non-linear optimization algorithms to solve the constrained optimization problem. The project will produce scalable open-loop and closed-loop control systems for optimizing applications for energy efficiency. The project includes presenting tutorials on information-efficient computing at conferences like Principles and Practice of Parallel Programming (PPoPP) and High-Performance Computer Architecture (HPCA), and incorporate this material into classes and make it publicly available. All data and outputs produced by the Capri project will be made available at this website: http://iss.ices.utexas.edu/.