A Dynamically Adaptable Bus Architecture for Trading-Off Among Performance, Consumption and Dependability in Cyber-Physical Systems

Cyber-Physical Systems need to handle increasingly complex tasks, which additionally, may have variable operating conditions over time. Therefore, dynamic resource management to adapt the system to different needs is required. In this paper, a new bus-based architecture, called ARTICo3, which by means of Dynamic Partial Reconfiguration, allows the replication of hardware tasks to support module redundancy, multi-thread operation or dual-rail solutions for enhanced side-channel attack protection is presented. A configuration-aware data transaction unit permits data dispatching to more than one module in parallel, or provide coalesced data dispatching among different units to maximize the advantages of burst transactions. The selection of a given configuration is application independent but context-aware, which may be achieved by the combination of a multi-thread model similar to the CUDA kernel model specification, combined with a dynamic thread/task/kernel scheduler. A multi-kernel application for face recognition is used as an application example to show one scenario of the ARTICo3 architecture

A Dynamically Adaptable Image Processing Application Trading Off Between High Performance, Consumption and Dependability in Real Time

As embedded systems evolve, problems inherent to technology become important limitations. In less than ten years, chips will exceed the maximum allowed power consumption affecting performance, since, even though the resources available per chip are increasing, frequency of operation has stalled. Besides, as the level of integration is increased, it is difficult to keep defect density under control, so new fault tolerant techniques are required. In this demo work, a new dynamically adaptable virtual architecture (ARTICo3) to allow dynamic and context-aware use of resources is implemented in a high performance Wireless Sensor node (HiReCookie) to perform an image processing application

Live Demonstration: A Dynamically Adaptable Image Processing Application Running in an FPGA-Based WSN Platform

This 1-Page Demonstration paper is included in the track “Multimedia Systems and Applications”. The work has been already published in [1] and [2]. The main idea of the demonstration is to show how the Virtual Architecture ARTICo3 works within a high performance wireless sensor node called HiReCookie. The selected demo includes an image processing application with several filters running as different kernels within the architecture ARTICo3. The virtual architecture works in a Spartan-6 FPGA included in the HiReCookie Node, [3] and [4]. During the demonstration, an image taken from a video camera attached to the node will be processed in real time by several dynamically reconfigurable kernels (median filters and edge detectors) under different working conditions. The solution scope includes solutions trading off among Low Power, Dependability and High Performance Computing

Execution modeling in self-aware FPGA-based architectures for efficient resource management

SRAM-based FPGAs have significantly improved their performance and size with the use of newer and ultra-deep-submicron technologies, even though power consumption, together with a time-consuming initial configuration process, are still major concerns when targeting energy-efficient solutions. System self-awareness enables the use of strategies to enhance system performance and power optimization taking into account run-time metrics. This is of particular importance when dealing with reconfigurable systems that may make use of such information for efficient resource management, such as in the case of the ARTICo3 architecture, which fosters dynamic execution of kernels formed by multiple blocks of threads allocated in a variable number of hardware accelerators, combined with module redundancy for fault tolerance and other dependability enhancements, e.g. side-channel-attack protection. In this paper, a model for efficient dynamic resource management focused on both power consumption and execution times in the ARTICo3 architecture is proposed. The approach enables the characterization of kernel execution by using the model, providing additional decision criteria based on energy efficiency, so that resource allocation and scheduling policies may adapt to changing conditions. Two different platforms have been used to validate the proposal and show the generalization of the model: a high-performance wireless sensor node based on a Spartan-6 and a standard off-the-shelf development board based on a Kintex-7

Design of OpenCL-compatible multithreaded hardware accelerators with dynamic support for embedded FPGAs

ARTICo3 is an architecture that permits to dynamically set an arbitrary number of reconfigurable hardware accelerators, each containing a given number of threads fixed at design time according to High Level Synthesis constraints. However, the replication of these modules can be decided at runtime to accelerate kernels by increasing the overall number of threads, add modular redundancy to increase fault tolerance, or any combination of the previous. An execution scheduler is used at kernel invocation to deliver the appropriate data transfers, optimizing memory transactions, and sequencing or parallelizing execution according to the configuration specified by the resource manager of the architecture. The model of computation is compatible with the OpenCL kernel execution model, and memory transfers and architecture are arranged to match the same optimization criteria as for kernel execution in GPU architectures but, differently to other approaches, with dynamic hardware execution support. In this paper, a novel design methodology for multithreaded hardware accelerators is presented. The proposed framework provides OpenCL compatibility by implementing a memory model based on shared memory between host and compute device, which removes the overhead imposed by data transferences at global memory level, and local memories inside each accelerator, i.e. compute unit, which are connected to global memory through optimized DMA links. These local memories provide unified access, i.e. a continuous memory map, from the host side, but are divided in a configurable number of independent banks (to increase available ports) from the processing elements side to fully exploit data-level parallelism. Experimental results show OpenCL model compliance using multithreaded hardware accelerators and enhanced dynamic adaptation capabilities

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Modern computing: vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Self-organising multi-agent control for distribution networks with distributed energy resources

Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time.Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time