Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Arvind Easwaran;
Pages: 33 - 33 Abstract: Cyber physical systems (CPSs) are the new generation of engineered embedded systems in which the sensing, computational, and physical processes are tightly coupled together to interact with the physical world and human operators. Many such CPS are deployed in safety-critical domains with hard real-time constraints, which makes it extremely important to guarantee their timeliness and reliability while maintaining good performance. Given the wide array of applications and their requirements, Real-Time Technologies in CPS is a multifaceted research area covering topics, such as data analytics, programming models, systems engineering, distributed computing and communication infrastructures, and systems software. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Abhishek Dubey;Gabor Karsai;Peter Volgyesi;Mary Metelko;Istvan Madari;Hao Tu;Yuhua Du;Srdjan Lukic;
Pages: 34 - 37 Abstract: This letter presents an overview of design mechanisms to abstract device access protocols in the resilient information architecture platform for smart grid, a middleware for developing distributed smart grid applications. These mechanisms are required to decouple the application functionality from the specifics of the device mechanisms built by the device vendors. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Volkmar Sieh;Robert Burlacu;Timo Hönig;Heiko Janker;Phillip Raffeck;Peter Wägemann;Wolfgang Schröder-Preikschat;
Pages: 38 - 41 Abstract: Predicting the temporal behavior of embedded real-time systems is a crucial but challenging task, as it is with the energetic behavior of energy-constrained systems, such as IoT devices. To carry out static analyses in order to determine the worst-case execution time or the worst-case energy consumption of tasks, cost models are inevitable. However, these models are rarely available on a fine-grained level for commercial-off-the-shelf hardware platforms. In this letter, we present NEO, an end-to-end toolchain that automatically generates cost models, which are then integrated into an existing static-analysis tool. NEO exploits automatically generated benchmark programs, which are measured on the target platform and investigated in a virtual machine. Based on the gathered data, we formulate mathematical optimization problems that eventually yield both worst-case execution-time and energy-consumption cost models. In our evaluations with an embedded hardware platform (e.g., ARM Cortex-M0+), we show that the open-source toolchain is able to precisely bound programs’ resources while achieving acceptable accuracy. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Boris Dreyer;Christian Hochberger;Thomas Ballenthin;Simon Wegener;
Pages: 42 - 45 Abstract: Precise execution time profiles (ETPs) are an extremely helpful instrument to assist software and system designers in analyzing the performance and timing behavior of embedded systems. Previously, we have presented an approach that exploits embedded trace units of modern system-on-chip. It allows us to compute execution time histograms during the runtime of the system under test (SuT). These histograms can easily be converted into ETPs. In this contribution, we show an extended version of this method that uses the information gathered in previous runs of the SuT to refine the binning used for the collection of the histograms. We show that these improved histograms deliver much more insight. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Leandro Poloni Dantas;Rodolfo J. de Azevedo;Salvador Pinillos Gimenez;
Pages: 46 - 49 Abstract: The use of hardware to perform part of central processing unit (CPU) processing functions is a consolidated practice that produces good results in terms of power and performance when applied in embedded systems. This letter describes the changes in the processor architecture to embed the functions of a microkernel to boost the performance of task-based systems. Part of the CPU overhead is caused by the microkernel to run the scheduler algorithm and context switching. Therefore, the microkernel’s functions, supported by a single additional internal register bank, were implemented by hardware to work in parallel with the CPU to reduce the task dispatch time. The experimental results show that by using this approach, the performance is virtually independent of the time slice, whereas the conventional approach (software implementation) is degraded by 79% as the time slice decreases. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Loris Duch;Soumya Basu;Miguel Peón-Quirós;Giovanni Ansaloni;Laura Pozzi;David Atienza;
Pages: 50 - 53 Abstract: Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-grained reconfigurable arrays (CGRAs) are good candidates for tackling these conflicting objectives, because thanks to their flexibility and high computational density they can efficiently support the computational hot-spots characterizing bio-digital signal processing applications. The interleaved-datapaths (i-DPs) CGRA presented in this letter further leverages the benefits of this architectural paradigm, focusing on ultralow energy operation. Its defining feature is the complex design of its computing cells, which, by embedding multiple i-DPs, allow a high ratio between computing and control logic, effectively speeding up computations and resulting in a marginal impact on the required integrated circuit area. i-DPs increase the energy efficiency of up to 33%, with respect to a single-DP alternative, when executing common kernels in the multilead electrocardiogram signal processing field. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Anuj Pathania;Jörg Henkel;
Pages: 54 - 57 Abstract: This letter presents a toolchain called HotSniper developed by integrating HotSpot temperature modeling tool with Sniper many-core simulator. HotSniper allows for interval thermal simulation of many-cores, which is several times faster than the cycle-accurate many-core thermal simulations and at the same time is more accurate than trace-based many-core thermal simulations. HotSniper toolchain provides efficient means to perform thermal-aware hardware–software codesign of many-core processors in domain of embedded systems. The source code for HotSniper has been made public. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Oswaldo Cadenas;Graham M. Megson;
Pages: 58 - 61 Abstract: A novel algorithm is proposed to compute the median of a running window of ${m}$ integers in ${O}$ (lg lg ${m}$ ) time. For a new window, the new median value is computed as a simple decision based on the previous median, and the values removed and inserted into the window. This facilitates implementations based on data structures that support fast ordinal predecessor/successor operations. The results show accelerations of up to factors of six for integer data streaming in typical embedded processors. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Authors:
Yonghee Yun;Sodam Han;Young Hwan Kim;
Pages: 62 - 65 Abstract: This letter proposes an effective speed-up estimation method for modern mobile devices. Unlike existing approaches, the proposed method uses a task graph to extract multiple parallelizable fractions of real-world mobile scenarios. Then, it uses the extracted parallelizable fractions to estimate the theoretical maximum speed-up of mobile devices. In experiments, the proposed method estimated the maximum speed-up of mobile devices more accurately than the state-of-the-art speed-up estimation method. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Pages: 68 - 68 Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. PubDate:
June 2019
Issue No:Vol. 11, No. 2 (2019)
Hybrid journal (It can contain Open Access articles)
