Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified

Sustainable Edge Computing: Challenges and Future Directions

An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data into the network. These requirements of IoT applications trigger the emergence of Edge computing paradigm. Currently, data centers are responsible for a global energy use between 2% and 3%. However, this trend is difficult to maintain, as bringing computing infrastructures closer to the edge of the network comes with its own set of challenges for energy efficiency. In this paper, we propose our approach for the sustainability of future computing infrastructures to provide (i) an energy-efficient and economically viable deployment, (ii) a fault-tolerant automated operation, and (iii) a collaborative resource management to improve resource efficiency. We identify the main limitations of applying Cloud-based approaches close to the data sources and present the research challenges to Edge sustainability arising from these constraints. We propose two-phase immersion cooling, formal modeling, machine learning, and energy-centric federated management as Edge-enabling technologies. We present our early results towards the sustainability of an Edge infrastructure to demonstrate the benefits of our approach for future computing environments and deployments.Comment: 26 pages, 16 figure

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions

Is Europe in the Driver's Seat? The Competitiveness of the European Automotive Embedded Systems Industry

This report is one of a series resulting from a project entitled ¿Competitiveness by Leveraging Emerging Technologies Economically¿ (COMPLETE), carried out by JRC-IPTS. Each of the COMPLETE studies illustrates in its own right that European companies are active on many fronts of emerging and disruptive ICT technologies and are supplying the market with relevant products and services. Nevertheless, the studies also show that the creation and growth of high tech companies is still very complex and difficult in Europe, and too many economic opportunities seem to escape European initiatives and ownership. COMPLETE helps to illustrate some of the difficulties experienced in different segments of the ICT industry and by growing potential global players. This report reflects the findings of a study conducted by Egil Juliussen and Richard Robinson, two senior experts from iSuppli Corporation on the Competitiveness of the European Automotive Embedded Software industry. The report starts by introducing the market, its trends, the technologies, their characteristics and their potential economic impact, before moving to an analysis of the competitiveness of the corresponding European industry. It concludes by suggesting policy options. The research, initially based on internal expertise and literature reviews, was complemented with further desk research, expert interviews, expert workshops and company visits. The results were ultimately reviewed by experts and also in a dedicated workshop. The report concludes that currently ICT innovation in the automotive industry is a key competence in Europe, with very little ICT innovation from outside the EU finding its way into EU automotive companies. A major benefit of a strong automotive ICT industry is the resulting large and valuable employment base. But future maintenance of automotive ICT jobs within the EU will only be possible if the EU continues to have high levels of product innovation.JRC.DDG.J.4-Information Societ

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Innovations in Electric Vehicle Technology: A Review of Emerging Trends and Their Potential Impacts on Transportation and Society

The adoption of electric vehicles (EVs) has gained significant momentum in recent years, driven by the need to reduce greenhouse gas emissions, improve air quality, and achieve sustainable transportation. This study presents a comprehensive review of emerging trends in EV technology and their potential impacts on transportation and society. The study explores various areas of innovation in the field of EVs, including battery technology, wireless charging, vehicle-to-grid (V2G) communication, lightweight materials, autonomous driving, vehicle-to-everything (V2X) communication, circular economy approaches, advanced charging infrastructure, energy storage, and social and behavioral innovations. This study reveals that battery technology advancements are driving the adoption of EVs. Lithium-ion batteries have improved energy density, charging speed, and lifespan. Alternative battery technologies, like solid-state and lithium-sulfur batteries, show promise for even higher energy density, faster charging, and increased safety. Wireless charging technology is emerging, with high-power and high-efficiency systems potentially addressing concerns about charging infrastructure and range anxiety. V2G communication allows EVs to serve as mobile energy storage units, contributing to grid stability, load balancing, and renewable energy integration. Lightweight materials, like advanced composites and lightweight metals, can significantly reduce the weight of EVs, improving energy efficiency and overall performance. Autonomous driving technologies have the potential to improve safety, reduce congestion, and optimize energy use. V2X communication enables a wide range of applications, like intelligent traffic management and enhanced safety features. Circular economy approaches, including designing EVs with recyclability and reusability in mind, using recycled materials in manufacturing, and developing end-of-life recycling and repurposing strategies, can minimize the environmental impact of EVs and contribute to their sustainability

Resilience viewed through the lens of climate change and water management

Resilience is not a new idea but there has been an upsurge in efforts to operationalize the concept within water management. This review begins with a synopsis of related themes around persistent and emerging pressures on freshwaters; environmental thresholds (or tipping points); ‘safe’ operating conditions; multiple stable states; regime shifts. A case is made for viewing and managing the resilience of water systems at nested scales. Indicators are needed to track evolving climate risks as well as to measure socio-ecological responses. Catchment properties can identify those river systems that are more or less likely to return to a pre-disturbance state; resilience further depends on institutional and social landscapes. Ideally, allied notions of resistance and reliability are applied alongside resilience to broaden the portfolio of adaptation measures. Water managers would also benefit from more consistent use of resilience terminology; incentives to build back better after catastrophes; strategic monitoring of incipient threats and tipping points; availability of long-term adaptation indicators; coordinated efforts to reduce non-climatic pressures on freshwaters (especially in headwaters); evidence-based, practical guidance on adaptation measures that build resilience