An Efficient Energy Aware Adaptive System-On-Chip Architecture For Real-Time Video Analytics

The video analytics applications which are mostly running on embedded devices have become prevalent in today’s life. This proliferation has necessitated the development of System-on-Chips (SoC) to perform utmost processing in a single chip rather than discrete components. Embedded vision is bounded by stringent requirements, namely real-time performance, limited energy, and Adaptivity to cope with the standards evolution. Additionally, to design such complex SoCs, particularly in Zynq All Programmable SoC, the traditional hardware/software codesign approaches, which rely on software profiling to perform the hardware/software partitioning, have fallen short of achieving this task because profiling cannot predict the performance of application on hardware, thus, a model that relates the application characteristics to the platform performance is inevitable. Delivering real-time performance for the fast-growing video resolutions while maintaining the architecture flexibility is non-viable on processors, Graphic Processing Unit, Digital Signal Processor, and Application Specific Integrated Circuit. Furthermore, with semiconductor technology scaling, increased power dissipation is expected; whereas, the battery capacity is not expected to increase significantly. A Performance model for Zynq is developed using analytical method and used in hardware/software codesign to facilitate algorithms mapping to hardware. Afterwards, an SoC for real-time video analytics is realized on Zynq using Harris corner detection algorithm. A careful analysis of the algorithm and efficient utilization of Zynq resources results in highly parallelized and pipelined architecture outperforms the state-of-the-art. Running on a developed energy-aware adaptive SoC and utilizing dynamic partial reconfiguration, a context-aware configuration scheduler adheres to operating context and trades off between video resolution and energy consumption to sustain the uttermost operation time while delivering real-time performance. A realtime corners detection at 79.8, 176.9, and 504.2 frame per second for HD1080, HD720, and VGA, respectively, is achieved which outperform the state-of-the-art for HD720 by 31 times and for VGA by 3.5 times. The scheduler configures, at run-time, the appropriate hardware that satisfies the operating context and user-defined constraints among the accelerators that are developed for HD1080, HD720, and VGA video standards. The self-adaptive method achieves 1.77 times longer operation time than a parametrized IP core for the same battery capacity, with negligible reconfiguration energy overhead. A marginal effect of reconfiguration time overhead is observed, for instance, only two video frames are dropped for HD1080p60 during the reconfiguration. Facilitating the design process by using analytical modeling, and the efficient utilization of Zynq resources along with self-adaptivity results in an efficient energyaware SoC that provides real-time performance for video analytics

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Cycle Accurate Energy and Throughput Estimation for Data Cache

Resource optimization in energy constrained real-time adaptive embedded systems highly depends on accurate energy and throughput estimates of processor peripherals. Such applications require lightweight, accurate mathematical models to profile energy and timing requirements on the go. This paper presents enhanced mathematical models for data cache energy and throughput estimation. The energy and throughput models were found to be within 95% accuracy of per instruction energy model of a processor, and a full system simulator?s timing model respectively. Furthermore, the possible application of these models in various scenarios is discussed in this paper

A New Approach for Quality Management in Pervasive Computing Environments

This paper provides an extension of MDA called Context-aware Quality Model Driven Architecture (CQ-MDA) which can be used for quality control in pervasive computing environments. The proposed CQ-MDA approach based on ContextualArchRQMM (Contextual ARCHitecture Quality Requirement MetaModel), being an extension to the MDA, allows for considering quality and resources-awareness while conducting the design process. The contributions of this paper are a meta-model for architecture quality control of context-aware applications and a model driven approach to separate architecture concerns from context and quality concerns and to configure reconfigurable software architectures of distributed systems. To demonstrate the utility of our approach, we use a videoconference system.Comment: 10 pages, 10 Figures, Oral Presentation in ECSA 201

Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing