Voltage island-driven floorplanning.

Ma, Qiang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 78-80).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Floorplanning --- p.2Chapter 1.3 --- Motivations --- p.4Chapter 1.4 --- Design Implementation of Voltage Islands --- p.5Chapter 1.5 --- Problem Formulation --- p.8Chapter 1.6 --- Progress on the Problem --- p.10Chapter 1.7 --- Contributions --- p.12Chapter 1.8 --- Thesis Organization --- p.14Chapter 2 --- Literature Review on MSV --- p.15Chapter 2.1 --- Introduction --- p.15Chapter 2.2 --- MSV at Post-floorplan/Post Placement Stage --- p.16Chapter 2.2.1 --- """Post-Placement Voltage Island Generation under Performance Requirement""" --- p.16Chapter 2.2.2 --- """Post-Placement Voltage Island Generation""" --- p.18Chapter 2.2.3 --- """Timing-Constrained and Voltage-Island-Aware Voltage Assignment""" --- p.19Chapter 2.2.4 --- """Voltage Island Generation under Performance Requirement for SoC Designs""" --- p.20Chapter 2.2.5 --- """An ILP Algorithm for Post-Floorplanning Voltage-Island Generation Considering Power-Network Planning""" --- p.21Chapter 2.3 --- MSV at Floorplan/Placement Stage --- p.22Chapter 2.3.1 --- """Architecting Voltage Islands in Core-based System-on-a- Chip Designs""" --- p.22Chapter 2.3.2 --- """Voltage Island Aware Floorplanning for Power and Timing Optimization""" --- p.23Chapter 2.4 --- Summary --- p.27Chapter 3 --- MSV Driven Floorplanning --- p.29Chapter 3.1 --- Introduction --- p.29Chapter 3.2 --- Problem Formulation --- p.32Chapter 3.3 --- Algorithm Overview --- p.33Chapter 3.4 --- Optimal Island Partitioning and Voltage Assignment --- p.33Chapter 3.4.1 --- Voltage Islands in Non-subtrees --- p.35Chapter 3.4.2 --- Proof of Optimality --- p.36Chapter 3.4.3 --- Handling Island with Power Down Mode --- p.37Chapter 3.4.4 --- Speedup in Implementation and Complexity --- p.38Chapter 3.4.5 --- Varying Background Chip-level Voltage --- p.39Chapter 3.5 --- Simulated Annealing --- p.39Chapter 3.5.1 --- Moves --- p.39Chapter 3.5.2 --- Cost Function --- p.40Chapter 3.6 --- Experimental Results --- p.40Chapter 3.6.1 --- Extension to Minimize Level Shifters --- p.45Chapter 3.6.2 --- Extension to Consider Power Network Routing --- p.46Chapter 3.7 --- Summary --- p.46Chapter 4 --- MSV Driven Floorplanning with Timing --- p.49Chapter 4.1 --- Introduction --- p.49Chapter 4.2 --- Problem Formulation --- p.52Chapter 4.3 --- Algorithm Overview --- p.56Chapter 4.4 --- Voltage Assignment Problem --- p.56Chapter 4.4.1 --- Lagrangian Relaxation --- p.58Chapter 4.4.2 --- Transformation into the Primal Minimum Cost Flow Problem --- p.60Chapter 4.4.3 --- Cost-Scaling Algorithm --- p.64Chapter 4.4.4 --- Solution Transformation --- p.66Chapter 4.5 --- Simulated Annealing --- p.69Chapter 4.5.1 --- Moves --- p.69Chapter 4.5.2 --- Speeding up heuristic --- p.69Chapter 4.5.3 --- Cost Function --- p.70Chapter 4.5.4 --- Annealing Schedule --- p.71Chapter 4.6 --- Experimental Results --- p.71Chapter 4.7 --- Summary --- p.72Chapter 5 --- Conclusion --- p.76Bibliography --- p.8

MORA: an Energy-Aware Slack Reclamation Scheme for Scheduling Sporadic Real-Time Tasks upon Multiprocessor Platforms

In this paper, we address the global and preemptive energy-aware scheduling problem of sporadic constrained-deadline tasks on DVFS-identical multiprocessor platforms. We propose an online slack reclamation scheme which profits from the discrepancy between the worst- and actual-case execution time of the tasks by slowing down the speed of the processors in order to save energy. Our algorithm called MORA takes into account the application-specific consumption profile of the tasks. We demonstrate that MORA does not jeopardize the system schedulability and we show by performing simulations that it can save up to 32% of energy (in average) compared to execution without using any energy-aware algorithm.Comment: 11 page

Power, Energy, and Thermal Management for Clustered Manycores

Efficient and effective system-level power, energy, and thermal management are very important issues in modern computing systems, for which clustered architectures with multiple voltage islands are an expected compromise between global and per-core DVFS. In this dissertation, we focus on two of the most relevant problems for such architectures, specifically, optimizing performance under power/thermal constraints, and minimizing energy under performance constraints

Parallel Evolutionary Algorithms for Energy Aware Scheduling

International audienceReducing energy consumption is an increasingly important issue in computing and embedded systems. In computing systems, minimizing energy consumption can significantly reduces the amount of energy bills. The demand for computing systems steadily increases and the cost of energy continues to rise. In embedded systems, reducing the use of energy allows to extend the autonomy of these systems. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. This chapter gives an overview of the main methods used to reduce the energy consumption in computing and embedded systems. As a use case and to give an example of a method, the chapter describes our new parallel bi-objective hybrid genetic algorithm that takes into account the completion time and the energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms

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

Clustering-Based Simultaneous Task and Voltage Scheduling for NoC Systems

Network-on-Chip (NoC) is emerging as a promising communication structure, which is scalable with respect to chip complexity. Meanwhile, latest chip designs are increasingly leveraging multiple voltage-frequency domains for energy-efficiency improvement. In this work, we propose a simultaneous task and voltage scheduling algorithm for energy minimization in NoC based designs. The energy-latency tradeoff is handled by Lagrangian relaxation. The core algorithm is a clustering based approach which not only assigns voltage levels and starting time to each task (or Processing Element) but also naturally finds voltage-frequency clusters. Compared to a recent previous work, which performs task scheduling and voltage assignment sequentially, our method leads to an average of 20 percent energy reduction

A Survey of Fault-Tolerance Techniques for Embedded Systems from the Perspective of Power, Energy, and Thermal Issues

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones