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

pTNoC: Probabilistically time-analyzable tree-based NoC for mixed-criticality systems

The use of networks-on-chip (NoC) in real-time safety-critical multicore systems challenges deriving tight worst-case execution time (WCET) estimates. This is due to the complexities in tightly upper-bounding the contention in the access to the NoC among running tasks. Probabilistic Timing Analysis (PTA) is a powerful approach to derive WCET estimates on relatively complex processors. However, so far it has only been tested on small multicores comprising an on-chip bus as communication means, which intrinsically does not scale to high core counts. In this paper we propose pTNoC, a new tree-based NoC design compatible with PTA requirements and delivering scalability towards medium/large core counts. pTNoC provides tight WCET estimates by means of asymmetric bandwidth guarantees for mixed-criticality systems with negligible impact on average performance. Finally, our implementation results show the reduced area and power costs of the pTNoC.The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under the PROXIMA Project (www.proxima-project.eu), grant agreement no 611085. This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Mladen Slijepcevic is funded by the Obra Social Fundación la Caixa under grant Doctorado “la Caixa” - Severo Ochoa. Carles Hern´andez is jointly funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and FEDER funds through grant TIN2014-60404-JIN. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717.Peer ReviewedPostprint (author's final draft

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

On the tailoring of CAST-32A certification guidance to real COTS multicore architectures

The use of Commercial Off-The-Shelf (COTS) multicores in real-time industry is on the rise due to multicores' potential performance increase and energy reduction. Yet, the unpredictable impact on timing of contention in shared hardware resources challenges certification. Furthermore, most safety certification standards target single-core architectures and do not provide explicit guidance for multicore processors. Recently, however, CAST-32A has been presented providing guidance for software planning, development and verification in multicores. In this paper, from a theoretical level, we provide a detailed review of CAST-32A objectives and the difficulty of reaching them under current COTS multicore design trends; at experimental level, we assess the difficulties of the application of CAST-32A to a real multicore processor, the NXP P4080.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal grant RYC-2013-14717.Peer ReviewedPostprint (author's final draft

FACT -- Operation of the First G-APD Cherenkov Telescope

Since more than two years, the First G-APD Cherenkov Telescope (FACT) is operating successfully at the Canary Island of La Palma. Apart from its purpose to serve as a monitoring facility for the brightest TeV blazars, it was built as a major step to establish solid state photon counters as detectors in Cherenkov astronomy. The camera of the First G-APD Cherenkov Telesope comprises 1440 Geiger-mode avalanche photo diodes (G-APD aka. MPPC or SiPM) for photon detection. Since properties as the gain of G-APDs depend on temperature and the applied voltage, a real-time feedback system has been developed and implemented. To correct for the change introduced by temperature, several sensors have been placed close to the photon detectors. Their read out is used to calculate a corresponding voltage offset. In addition to temperature changes, changing current introduces a voltage drop in the supporting resistor network. To correct changes in the voltage drop introduced by varying photon flux from the night-sky background, the current is measured and the voltage drop calculated. To check the stability of the G-APD properties, dark count spectra with high statistics have been taken under different environmental conditions and been evaluated. The maximum data rate delivered by the camera is about 240 MB/s. The recorded data, which can exceed 1 TB in a moonless night, is compressed in real-time with a proprietary loss-less algorithm. The performance is better than gzip by almost a factor of two in compression ratio and speed. In total, two to three CPU cores are needed for data taking. In parallel, a quick-look analysis of the recently recorded data is executed on a second machine. Its result is publicly available within a few minutes after the data were taken. [...]Comment: 19th IEEE Real-Time Conference, Nara, Japan (2014