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

Using genetic evolutionary software application testing to verify a DSP SoC

Copyright © 2008 IEEEA digital signal processor (DSP) system-on-chip (SoC) can be designed using a variety of architectures and techniques. This often presents different verification challenges compared to conventional SoC or processor designs. Verification of such designs should take into account the goals and applications of the DSP, and how they are eventually used. This paper proposes an application based verification methodology and demonstrates this technique on a real-life DSP SoC design. Our technique employs a library of specially devised application functions as test building blocks, followed by a genetic evolutionary test generator to compose these application functions into effective test programs.Adriel Cheng, Cheng-Chew Lim, Yihe Sun, Hu He, Zhixiong Zhou, Ting Le

SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose a Self-Aware Resource Allocation (SARA) framework for heterogeneous MPSoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores.Comment: Accepted by the 55th annual Design Automation Conference 2018 (DAC'18