Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

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

A fuzzy logic based dynamic reconfiguration scheme for optimal energy and throughput in symmetric chip multiprocessors

Embedded systems architectures have traditionally often been investigated and designed in order to achieve a greater throughput combined with minimum energy consumption. With the advent of reconfigurable architectures it is now possible to support algorithms to find optimal solutions for an improved energy and throughput balance. As a result of ongoing research several online and offline techniques and algorithm have been proposed for hardware adaptation. This paper presents a novel coarse-grained reconfigurable symmetric chip multiprocessor (SCMP) architecture managed by a fuzzy logic engine that balances performance and energy consumption. The architecture incorporates reconfigurable level 1 (L1) caches, power gated cores and adaptive on-chip network routers to allow minimizing leakage energy effects for inactive components. A coarse grained architecture was selected as to be a focus for this study as it typically allows for fast reconfiguration as compared to the fine-grained architectures, thus making it more feasible to be used for runtime adaption schemes. The presented architecture is analyzed using a set of OpenMP based parallel benchmarks and the results show significant improvements in performance while maintaining minimum energy consumption

Energy-efficient Static Task Scheduling on VFI based NoC-HMPSoCs for Intelligent Edge Devices in Cyber-Physical Systems

The interlinked processing units in the modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC) based multiprocessor system-on-chip (MPSoC) architecture is becoming a de-facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, we investigate energy and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI based NoC-MPSoCs with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We have introduced a notion of Energy Gradient (EG) that guides EGD in its search for islands voltage settings and minimize the total energy consumption. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ~ 24% and ~ 30% over CA-TMES-Search and CA-TMES-Quick respectively

Low-energy Scheduling Algorithms for Wearable Fall Pre-impact Detection System

ABSTRACT In this paper, novel low-energy static and dynamic scheduling algorithms with low computational complexities for heterogeneous multiprocessor systems are proposed. Since battery life of the system plays a critical role in wearable embedded systems, the algorithms are useful for energy consumption reduction in Body Area Network (BAN)-based wearable multiprocessor systems in healthcare applications. Our developed BAN-based fall pre-impact detection system is used in this investigation. Based on simulation results using the algorithms, it is found that the battery life can be extended up to 41.6 percent more of its normal life without the algorithms

A Fuzzy Logic Reconfiguration Engine for Symmetric Chip Multiprocessors

Recent developments in reconfigurable multiprocessor system on chip (MPSoC) have offered system designers a great amount of flexibility to exploit task concurrency with higher throughput and less energy consumption. This paper presents a novel fuzzy logic reconfiguration engine (FLRE) for coarse grain MPSoC reconfiguration that facilitates to identify an optimum balance between power and performance of the system. The FLRE is composed on two levels of abstraction layers. The system selects an optimal configuration of Level 1 / Level 2 cache size and Associativity, processor operating frequency and voltage, the number of cores based on miss rate, and energy and throughput information of the system both at core and SoC level. An 8-core symmetric chip multiprocessor has been used to evaluate the proposed scheme. The results show an overall decrease of energy consumption with not more than 30% decrease in the throughput

DESIGN METHODOLOGIES FOR RELIABLE AND ENERGY-EFFICIENT MULTIPROCESSOR SYSTEM

Ph.DDOCTOR OF PHILOSOPH

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling