Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

abstract: Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6x reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A Temperature and Reliability Oriented Simulation Framework for Multi-core Architectures

The increasing complexity of multi-core architectures demands for a comprehensive evaluation of different solutions and alternatives at every stage of the design process, considering different aspects at the same time. Simulation frameworks are attractive tools to fulfil this requirement, due to their flexibility. Nevertheless, state-of-the-art simulation frameworks lack a joint analysis of power, performance, temperature profile and reliability projection at system-level, focusing only on a specific aspect. This paper presents a comprehensive estimation framework that jointly exploits these design metrics at system-level, considering processing cores, interconnect design and storage elements. We describe the framework in details, and provide a set of experiments that highlight its capability and flexibility, focusing on temperature and reliability analysis of multi-core architectures supported by Network-on-Chip interconnect

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems