Dynamic Energy and Thermal Management of Multi-Core Mobile Platforms: A Survey

Multi-core mobile platforms are on rise as they enable efficient parallel processing to meet ever-increasing performance requirements. However, since these platforms need to cater for increasingly dynamic workloads, efficient dynamic resource management is desired mainly to enhance the energy and thermal efficiency for better user experience with increased operational time and lifetime of mobile devices. This article provides a survey of dynamic energy and thermal management approaches for multi-core mobile platforms. These approaches do either proactive or reactive management. The upcoming trends and open challenges are also discussed

MPSoCBench : um framework para avaliação de ferramentas e metodologias para sistemas multiprocessados em chip

Orientador: Rodolfo Jardim de AzevedoTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Recentes metodologias e ferramentas de projetos de sistemas multiprocessados em chip (MPSoC) aumentam a produtividade por meio da utilização de plataformas baseadas em simuladores, antes de definir os últimos detalhes da arquitetura. No entanto, a simulação só é eficiente quando utiliza ferramentas de modelagem que suportem a descrição do comportamento do sistema em um elevado nível de abstração. A escassez de plataformas virtuais de MPSoCs que integrem hardware e software escaláveis nos motivou a desenvolver o MPSoCBench, que consiste de um conjunto escalável de MPSoCs incluindo quatro modelos de processadores (PowerPC, MIPS, SPARC e ARM), organizado em plataformas com 1, 2, 4, 8, 16, 32 e 64 núcleos, cross-compiladores, IPs, interconexões, 17 aplicações paralelas e estimativa de consumo de energia para os principais componentes (processadores, roteadores, memória principal e caches). Uma importante demanda em projetos MPSoC é atender às restrições de consumo de energia o mais cedo possível. Considerando que o desempenho do processador está diretamente relacionado ao consumo, há um crescente interesse em explorar o trade-off entre consumo de energia e desempenho, tendo em conta o domínio da aplicação alvo. Técnicas de escalabilidade dinâmica de freqüência e voltagem fundamentam-se em gerenciar o nível de tensão e frequência da CPU, permitindo que o sistema alcance apenas o desempenho suficiente para processar a carga de trabalho, reduzindo, consequentemente, o consumo de energia. Para explorar a eficiência energética e desempenho, foram adicionados recursos ao MPSoCBench, visando explorar escalabilidade dinâmica de voltaegem e frequência (DVFS) e foram validados três mecanismos com base na estimativa dinâmica de energia e taxa de uso de CPUAbstract: Recent design methodologies and tools aim at enhancing the design productivity by providing a software development platform before the definition of the final Multiprocessor System on Chip (MPSoC) architecture details. However, simulation can only be efficiently performed when using a modeling and simulation engine that supports system behavior description at a high abstraction level. The lack of MPSoC virtual platform prototyping integrating both scalable hardware and software in order to create and evaluate new methodologies and tools motivated us to develop the MPSoCBench, a scalable set of MPSoCs including four different ISAs (PowerPC, MIPS, SPARC, and ARM) organized in platforms with 1, 2, 4, 8, 16, 32, and 64 cores, cross-compilers, IPs, interconnections, 17 parallel version of software from well-known benchmarks, and power consumption estimation for main components (processors, routers, memory, and caches). An important demand in MPSoC designs is the addressing of energy consumption constraints as early as possible. Whereas processor performance comes with a high power cost, there is an increasing interest in exploring the trade-off between power and performance, taking into account the target application domain. Dynamic Voltage and Frequency Scaling techniques adaptively scale the voltage and frequency levels of the CPU allowing it to reach just enough performance to process the system workload while meeting throughput constraints, and thereby, reducing the energy consumption. To explore this wide design space for energy efficiency and performance, both for hardware and software components, we provided MPSoCBench features to explore dynamic voltage and frequency scalability (DVFS) and evaluated three mechanisms based on energy estimation and CPU usage rateDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Side-Channel Protected MPSoC through Secure Real-Time Networks-on-Chip

The integration of Multi-Processors System-on-Chip (MPSoCs) into the Internet -of -Things (IoT) context brings new opportunities, but also represent risks. Tight real-time constraints and security requirements should be considered simultaneously when designing MPSoCs. Network-on-Chip (NoCs) are specially critical when meeting these two conflicting characteristics. For instance the NoC design has a huge influence in the security of the system. A vital threat to system security are so-called side-channel attacks based on the NoC communication observations. To this end, we propose a NoC security mechanism suitable for hard real-time systems, in which schedulability is a vital design requirement. We present three contributions. First, we show the impact of the NoC routing in the security of the system. Second, we propose a packet route randomisation mechanism to increase NoC resilience against side-channel attacks. Third, using an evolutionary optimisation approach, we effectively apply route randomisation while controlling its impact on hard real-time performance guarantees. Extensive experimental evidence based on analytical and simulation models supports our findings

Performance and Energy Trade-offs Analysis of L2 on-Chip Cache Architectures for Embedded MPSoCs

On-chip memory organization is one of the most important aspects that can influence the overall system behavior in multi-processor systems. Following the trend set by high-performance processors, high-end embedded cores are moving from single-level on chip caches to a two-level on-chip cache hierarchy. Whereas in the embedded world there is general consensus on L1 private caches, for L2 there is still not a dominant architectural paradigm. Cache architectures that work for high performance computers turn out to be inefficient for embedded systems (mainly due to power-efficiency issues). This paper presents a virtual platform for design space exploration of L2 cache architectures in low-power Multi-Processor-Systems-on-Chip (MPSoCs). The tool contains several L2 caches templates, and new architectures can be easily added using our flexible plugin system. Given a set of constrains for a specific system (power, area, performance), our tool will perform extensive exploration to find the cache organization that best suits our needs. Through some practical experiments, we show how it is possible to select the optimal L2 cache, and how this kind of tool can help designers avoid some common misconceptions. Benchmarking results in the experiments section will show that for a case study with multiple processors running communicating tasks allocated on different cores, the private L2 cache organization still performs better than the shared one

CROSS-LAYER DESIGN, OPTIMIZATION AND PROTOTYPING OF NoCs FOR THE NEXT GENERATION OF HOMOGENEOUS MANY-CORE SYSTEMS

This thesis provides a whole set of design methods to enable and manage the runtime heterogeneity of features-rich industry-ready Tile-Based Networkon- Chips at different abstraction layers (Architecture Design, Network Assembling, Testing of NoC, Runtime Operation). The key idea is to maintain the functionalities of the original layers, and to improve the performance of architectures by allowing, joint optimization and layer coordinations. In general purpose systems, we address the microarchitectural challenges by codesigning and co-optimizing feature-rich architectures. In application-specific NoCs, we emphasize the event notification, so that the platform is continuously under control. At the network assembly level, this thesis proposes a Hold Time Robustness technique, to tackle the hold time issue in synchronous NoCs. At the network architectural level, the choice of a suitable synchronization paradigm requires a boost of synthesis flow as well as the coexistence with the DVFS. On one hand this implies the coexistence of mesochronous synchronizers in the network with dual-clock FIFOs at network boundaries. On the other hand, dual-clock FIFOs may be placed across inter-switch links hence removing the need for mesochronous synchronizers. This thesis will study the implications of the above approaches both on the design flow and on the performance and power quality metrics of the network. Once the manycore system is composed together, the issue of testing it arises. This thesis takes on this challenge and engineers various testing infrastructures. At the upper abstraction layer, the thesis addresses the issue of managing the fully operational system and proposes a congestion management technique named HACS. Moreover, some of the ideas of this thesis will undergo an FPGA prototyping. Finally, we provide some features for emerging technology by characterizing the power consumption of Optical NoC Interfaces

Novel DVFS Methodologies For Power-Efficient Mobile MPSoC

Low power mobile computing systems such as smartphones and wearables have become an integral part of our daily lives and are used in various ways to enhance our daily lives. Majority of modern mobile computing systems are powered by multi-processor System-on-a-Chip (MPSoC), where multiple processing elements are utilized on a single chip. Given the fact that these devices are battery operated most of the times, thus, have limited power supply and the key challenges include catering for performance while reducing the power consumption. Moreover, the reliability in terms of lifespan of these devices are also affected by the peak thermal behaviour on the device, which retrospectively also make such devices vulnerable to temperature side-channel attack. This thesis is concerned with performing Dynamic Voltage and Frequency Scaling (DVFS) on different processing elements such as CPU & GPU, and memory unit such as RAM to address the aforementioned challenges. Firstly, we design a Computer Vision based machine learning technique to classify applications automatically into different categories of workload such that DVFS could be performed on the CPU to reduce the power consumption of the device while executing the application. Secondly, we develop a reinforcement learning based agent to perform DVFS on CPU and GPU while considering the user's interaction with such devices to optimize power consumption and thermal behaviour. Next, we develop a heuristic based automated agent to perform DVFS on CPU, GPU and RAM to optimize the same while executing an application. Finally, we explored the affect of DVFS on CPUs leading to vulnerabilities against temperature side-channel attack and hence, we also designed a methodology to secure against such attack while improving the reliability in terms of lifespan of such devices

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo