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Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evaluating the subjective risk level of various decisions can improve ADS' safety in both normal and complex driving scenarios. However, existing deep learning-based methods often fail to model the relationships between traffic participants and can suffer when faced with complex real-world scenarios. Besides, these methods lack transferability and explainability. To address these limitations, we propose a novel data-driven approach that uses scene-graphs as intermediate representations. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers for modeling the subjective risk of driving maneuvers. To train our model, we formulate this task as a supervised scene classification problem. We consider a typical use case to demonstrate our model's capabilities: lane changes. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) synthesized datasets, also illustrating that our approach can learn effectively even from smaller datasets. We also show that our model trained on a synthesized dataset achieves an average accuracy of 87.8% when tested on a real-world dataset compared to the 70.3% accuracy achieved by the state-of-the-art model trained on the same synthesized dataset, showing that our approach can more effectively transfer knowledge. Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2.7% and 0.7% respectively, and increases its explainability., 10 pages, 5 figures, 1 table

A neuro-cognitive inspired architecture based on the Hierarchical Temporal Memory (HTM) is proposed for anomaly detection and simultaneous data prediction in real-time for smart grid μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection and simultaneous data prediction in real-time. Our results show that the proposed HTM can predict anomalies within 83%-90% accuracy for three different application profiles, namely Standard, Reward Few False Positive, Reward Few False Negative for two different datasets. We show that the HTM is competitive to five state-of-the-art algorithms for anomaly detection. Moreover, for the multi-step prediction in the online setting, the same HTM achieves a low 0.0001 normalized mean square error, a low negative log-likelihood score of 1.5 and is also competitive to six state-of-the-art prediction algorithms. We demonstrate that the same HTM model can be used for both the tasks and can learn online in one-pass, in an unsupervised fashion and adapt to changing statistics. The other state-of-the-art algorithms are either less accurate or are limited to one of the tasks or cannot learn online in one-pass, and adapt to changing statistics.

In cyber-physical additive manufacturing systems, side-channel attacks have been used to reconstruct the G/M-code (which are instructions given to a manufacturing system) of 3D objects being produced. This method is effective for stealing intellectual property from an organization, through least expected means, during prototyping stage before the product goes through a large-scale fabrication and comes out in the market. However, an attacker can be far from being able to completely reconstruct the G/M-code due to lack of enough information leakage through the side-channels. In this paper, we propose a novel way to amplify the information leakage and thus boost the chances of recovery of G/M-code by surreptitiously altering the compiler. By using this compiler, an adversary may easily control various parameters to magnify the leakage of information from a 3D printer while still producing the desired object, thus remaining hidden from the authentic users. This type of attack may be implemented by strong attackers having access to the tool chain and seeking high level of stealth. We have implemented such a compiler and have demonstrated that it increases the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39 percent compared to previously proposed attacks.

Aggressive time-to-market constraints and enormous hardware design and fabrication costs have pushed the semiconductor industry toward hardware Intellectual Properties (IP) core design. However, the globalization of the integrated circuits (IC) supply chain exposes IP providers to theft and illegal redistribution of IPs. Watermarking and fingerprinting are proposed to detect IP piracy. Nevertheless, they come with additional hardware overhead and cannot guarantee IP security as advanced attacks are reported to remove the watermark, forge, or bypass it. In this work, we propose a novel methodology, GNN4IP, to assess similarities between circuits and detect IP piracy. We model the hardware design as a graph and construct a graph neural network model to learn its behavior using the comprehensive dataset of register transfer level codes and gate-level netlists that we have gathered. GNN4IP detects IP piracy with 96% accuracy in our dataset and recognizes the original IP in its obfuscated version with 100% accuracy.

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.

Recent decades have observed the proliferation of sensors in embedded and cyber-physical systems (ECPSs). Sensors are an essential part of embedded and CPSs and serve as a bridge between physical quantities and connected systems. The tight coupling between sensors and systems enables many critical applications where decisions are taken by using the information from various sensors at different time-scales. This tight coupling opens the “Pandora's Box” of unknown threats that could come from very unconventional ways. An unconventional attack model could be to noninvasively attack sensors using forged spoofing signals and trigger unwanted behavior in connected systems. This paper introduces this type of new, strong, and unorthodox attack model and elaborates how important this will be in the near future when sensors will pervade our lives. Moreover, this paper presents a motivational example of a sensor-spoofing attack on Hall sensors in the context of smart grids to demonstrate the harmful consequences of this type of attack in ECPSs.

Wireless communication is being used as an enabling technology with traditional fixed traffic control systems in this transitional era toward Intelligent Transportation Systems (ITS). Unfortunately, major security concerns have arisen with respect to ever-increasing complexity and interconnectivity, and a noticeable lack of attention for security in these systems. Addressing concerns is a colossal challenge as it requires thorough development and formal analysis of a system model with respect to security. To tackle this challenge, we present a novel formal attack modeling and impact analysis methodology based on the Link Queue Model (LQM) of traffic flow inside a double ring road network, which is equivalent to a grid network with homogeneous links. We develop attack models as functions of tampered traffic control settings (e.g., green time ratios, cycle length, retaining ratios) with outputs equivalent to mobility impacts on the traffic network (e.g., time until system reaches state convergence, asymptotic average network flow). Further, for a given attack model, we define and identify vulnerable states: states that are critical to protect because they lead to negative impacts under the given attack model. Using our methodology we found that for certain vulnerable states, after only a few cycles of tampered control settings an attacker could cause a real impact of 1.5x speed-up in gridlock state convergence or 37%-99% drop in the asymptotic average flow rate. These results imply potentially drastic financial costs for cities and all involved drivers if similar attacks were performed on a real traffic control system.

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in real-time.Our numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. The performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.

Graphics Processing Units (GPUs) have been employed in embedded systems to handle increased amounts of computation and to satisfy the timing requirement. Due to the small feature size, chip aging and within-die parameter variations have been considered to be among the challenging problems for state-of-the-art processors, including GPUs. In order to deal with the process variation, several processors use chip-level guardbanding, which uses the lowest operating frequency that results in a significant chip-level performance drop. Other processors improve their performance efficiency through core-level guardbanding that may use a different operating frequency for each core. Existing aging management techniques are based on the chip-level guardbanding, which assigns the same number of instructions to the cores that have the same aging status. However, in the presence of the process variation, existing aging management techniques have a limitation in minimizing the aging effect because each core has a different amount of stress for the same number of instructions. In order to tackle this problem, we propose a low-overhead aging and process variation aware workload management technique for embedded GPUs. The proposed technique considers the process variation and the current aging status together, and assigns a different number of instructions to clusters to minimize the aging effect in the presence of process variation. Results show that our technique improves the GPU aging in over 95 percent of cases whereas the state-of-the-art compiler-based technique improves the GPU aging in 72.25 percent of cases. Moreover, compared to the compiler-based technique, our technique reduces the performance overhead by 40 percent while achieving almost the same GPU aging improvement.

Battery (sub)systems are used in many systems (systems-of-systems) in the Internet of Things (IoT) ranging from everyday ones (e.g., mobile systems, home appliances, etc.) to safety-critical and/or mission-critical ones (e.g., electrical vehicles, unmanned aerial vehicles, autonomous underwater vehicles, etc.). As these systems become more interconnected with each other and their environments and batteries become more energy dense, the safety risks of using batteries increase. To guarantee effectiveness and prevent potential safety threats (i.e., failure, overheating, explosion), it is not only crucial to ensure that batteries are functioning correctly (via safety circuits and battery management system), but to also prevent security threats that specifically target the battery system from different parts of these systems. A security analysis is necessary for system manufacturers and users to understand what threats and solutions exist for battery system security. In this paper, we present a security perspective on battery systems, where we use a layered approach to analyze vulnerabilities, threats, and potential effects. We divide the battery system into the Physical, Battery Management System, and Application layers and use mobile systems and cyber-physical systems as case studies for IoT applications. We then highlight and discuss some existing solutions and mention the potential research directions on battery system security.

The demand for low-power and high-performance computing has been driving the semiconductor industry for decades. The semiconductor technology has been scaled down to satisfy these demands. At the same time, the semiconductor technology has faced severe reliability challenges like soft-error. Research has been conducted to improve the soft-error reliability of the GPU, which has been improved by using various methodologies such as redundancy methodologies. However, the GPU compiler has yet to be considered for improving the soft-error reliability of the GPU. In this paper, in order to improve the soft-error reliability of the GPU, we propose a novel GPU architecture aware compilation methodology. The proposed methodology jointly considers the parallel behavior of the GPU hardware and the applications, and minimizes the vulnerability of the GPU applications during instruction scheduling. In addition, the proposed methodology is able to complement any hardware based soft-error reliability improvement techniques. We compared our compilation methodology with the state-of-the-art soft-error reliability aware techniques and the performance aware instruction scheduling. We have injected the soft-errors during the experiments and have compared the number of correct executions that have no erroneous output. Our methodology requires less performance and power overhead than the state-of-the-art soft-error reliability methodologies in most cases. Compilation time overhead of our methodology is 8.13 seconds on average. The experimental results show that our methodology improves the soft-error reliability by 23 percent and 12 percent (up to 64 percent and 52 percent) compared to the state-of-the-art soft-error reliability and performance aware compilation techniques, respectively. Moreover, we have shown that the soft-error reliability of a GPU is not related to the performance, but to the fine-grained timing behavior of an application.

In previous chapter, we presented a data-driven acoustic attack model. However, in the previous attack model, an attacker may be far from being able to completely reconstruct the G/M-code due to lack of enough information leakage through the side-channels. In this chapter, we present a novel way to amplify the information leakage and thus boost the chances of recovery of G/M-code by surreptitiously altering the compiler. By using this compiler, an adversary may easily control various parameters to magnify the leakage of information from a 3D printer while still producing the desired object, thus remaining hidden from the authentic users. This type of attack may be implemented by strong attackers having access to the tool chain and seeking high level of stealth. We present an implementation of such a compiler here and demonstrate how it can increase the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39% compared to previously proposed data-driven attack model.

In this chapter, we will present a data-driven security framework for modeling the cross-domain security of cyber-physical production systems. Specifically, we will present a novel conditional generative adversarial network-based modeling approach to abstract and estimate the relations between the cyber and physical domains. Using this framework, we will demonstrate how we can determine if various security requirements such as confidentiality, availability, and integrity are met. We will analyze the proposed framework for performing a security analysis of a cyber-physical additive manufacturing system.

In Chap. 9, we presented a structural graph convolutional neural network which is capable of performing supervising learning to estimate a function between non-euclidean data and categorical data. In this chapter, we focus on non-euclidean data which are evolving over time. In the cyber-physical system, most of the non-euclidean data (such as engineering data, energy, and signal flow graph, call graph of the firmware, etc.) are always evolving. Hence, it is necessary to utilize algorithms that are capable of handling such temporally evolving non-euclidean data. In this chapter, we present a novel dynamic graph embedding algorithm to handle this issue. In the rest of the chapter, we consider temporally evolving graphs as the non-euclidean data and present an algorithm capable of capturing the pattern of time-varying links.

Cyber-Physical Production Systems (CPPS) will usher a new era of smart manufacturing. However, CPPS will be vulnerable to cross-domain attacks due to the interactions between the cyber and physical domains. To address the challenges of modeling cross-domain security in CPPS, we are proposing GAN-Sec, a novel conditional Generative Adversarial Network based modeling approach to abstract and estimate the relations between the cyber and physical domains. Using GAN-Sec, we are able to determine if various security requirements such as confidentiality, availability, and integrity are met. We provide a security analysis of an additive manufacturing system to demonstrate the applicability of GAN-Sec.

Modern and Emergent automotive systems are highly complex, dominated by a large number of integrated electronics and software components. The electronic and software components include a diversity of device functionality, ranging across infotainment, driver assistance, radio, and wireless communication. The future is expected to see an even more explosive increase in complexity with the emergence of fully automated connected cars that need to have continuous communication with a smart highway system, interact with a variety of structured and ad hoc networks with different levels of trustworthiness, and make real-time analytics in the context of an in-motion, rapidly changing environment. In this complex world, it is critical to ensure that these systems behave predictably, securely, and reliably, even in an environment involving interaction with millions of other, potentially malicious, computing agents. A hacked automotive system, in the world of self-driving vehicles and vehicles with automated driver assistance, can cause catastrophic consequences, including significant loss of human lives, breakdown of highway systems, and shut-down of an entire city or region. Indeed, automotive systems do (and must) require some of the most stringent levels of compliance with requirements from security. On the other hand, the high complexity of the systems makes enforcement of such standards a highly challenging exercise.

The microgrid has been shown to be profitable, reliable, and efficient for military, commercial, and university-like installations. However, until now, there has been no study to show how and when a residential microgrid may be profitable. Therefore, in this paper, we present a design space exploration methodology of the microgrid by modeling all the energy resources at the residential level and conducting numerous simulations with various parameters. Moreover, a set of rules are defined to make the stakeholders in the microgrid profitable. Also, by analyzing the number of houses in the microgrid, we observe that the number of years it takes to return the capital costs invested in the microgrid may become adequately short for a certain range of the number of houses. For instance, if the aggregator owns the renewable energy resources, e.g., solar panels, it may profit in less than five years when 500 houses participate in the microgrid where each house owns 500 sf solar panels. On the other hand, if the prosumers own the renewable energy resources, e.g., solar panels, the aggregator may profit in about a year. Typically, for an apartment-block type housing area in U.S. there are more than 1000 houses, therefore the aggregator profitability may improve furthermore.

Software-integrated multidomain automotive systems, which is also referred to as automotive cyber–physical systems (CPS) consist of various interacting domains (software, hardware, multiphysics, communication, etc.). Design decisions in one domain may completely change the constraints and requirements in the other domains, e.g., adding more functions in a modern automotive CPS may require changes to thousands of lines of software code or even the mechanical architecture. Existing CPS design methodologies are siloed in a specific domain and therefore have limited design space exploration capabilities because only one domain can be tested at a time. This paper presents a functional-level cyber–physical codesign methodology starting from the functional model of the CPS capable of concurrently expressing (multi-)physics and control in automotive applications. Moreover, we introduce a high-level synthesis algorithm capable of selecting a set of optimized system architectures using various executable simulation components and cost metrics. We demonstrate our methodology with a realistic automotive use case and explore various design alternatives for implementing the control systems in pure continuous domain (e.g., traditional automotive subsystems without engine control units) or hybrid domain (e.g., brake-by-wire, steer-by-wire, drive-by-wire, etc.) under power, performance, and reliability constraints.

Securing cyber-physical systems (CPS) is an active area of research. For example, manufacturing and military CPS have been traditionally designed without an emphasis on cybersecurity. This paper presents a model-based secure-by-design approach for modeling and simulating the cybersecurity aspects of CPS. We demonstrate our systematic approach by modeling several classes of cyberattacks that may affect the normal operations of CPS, and evaluate the impact of these attacks on the system through the use of simulation. We follow a functional modeling approach that may reduce the engineering effort and increase the quality of the developed system, while also increasing the resilience of the system when exposed to cyberattacks.

GPUs have been employed in the embedded systems to handle increased amount of computation and satisfy the timing requirement. Therefore, the lifetime of embedded GPUs is considered one of the most important aspects to ensure functional correctness over a long period of time. Moreover, existing state-of-the-art compiler-based GPU aging management techniques suffer from a considerable amount of performance overhead. In this paper, we propose a low-overhead aging-aware resource management technique. The proposed technique extends the behavior of the existing warp scheduler and the instruction dispatcher to cluster the computational cores and distribute instructions based on the aging information. Compared to when using the original applications, our technique improves the aging of the embedded GPU by 30% on average. In addition, compared to the state-of-the-art GPU aging management technique, our technique reduces the performance overhead by 16.4% on average while improving the aging by 3% on average.

A modern automotive design contains over a hundred microprocessors, several cyber-physical modules, connectivity to a variety of networks, and several hundred megabytes of software. The future is anticipated to see an even sharper rise in complexity of this electronics, with the imminence of driverless vehicles, the potential of connected automobiles within a few years, and work towards seamless integration of automobiles with smart cities and infrastructure systems. Security is a fundamental challenge in the design of automotive systems. Unfortunately, security considerations in automotive systems are complicated by two factors: (1) need for real-time mitigation against in-field threats; and (2) in-field configurability and extensibility of security features. This paper examines the trade-offs between security countermeasures, real-time requirements, and in-field configurability needs for modern automotive systems. We discuss the current state of the practice in automotive security architecture, as well as gaps and challenges that need to be addressed for a viable security solution in future.

Cyber-Physical Energy Systems (CPES) are introduced for different levels (e.g., microgrid and home) of residential, commercial, and industrial domains. Different management methodologies are implemented using Internet-of-Things (IoT) to address their challenges. IoT has enabled the required interconnectivity for the devices in these systems. However, the management methodologies require multitude of different types of sensing and actuating devices which generate and process large sensed data. Hence, the complexity, scalability, heterogeneity, and performance of the methodologies become challenging with the growing number of these various devices. Implementing the control and management, e.g., energy management-as-a-service for these systems has been seen as a solution providing the required scalability, interactivity, and customizability of architecture. On the other hand, fog computing brings the computation (intelligence) close to the networking platform (edge) benefiting us to address interactivity, scalability, complexity, and inherent heterogeneity challenges. Proximity of the computation to the devices may improve the performance and dependability for delay-sensitive devices. Hence, fog computing has been seen as a promising platform for implementing control-as-a-service (CaaS) in the IoT and CPES. Herein, as an example, we illustrate an energy management-as-a-service implemented on a fog computing platform for a residential CPES.

Security is important in IoT as any other computing systems. However, standard solutions are necessary but not sufficient due to the added dimensions of their presence in physical space and the possibility for them to interact. This talk presents a new set of challenges on security in IoT systems that compose synergistically. We illustrate the problems and approaches using collaborative indoor localization on resource-constrained nodes as a representative example.

Additive manufacturing systems leak cyber-related information (such as G-code, M-code, etc.) from the side-channels (such as acoustic, power, thermal, etc.). In our work, we have successfully demonstrated the vulnerability of additive manufacturing to thermal side-channel attacks, where confidentiality can be breached to steal the Intellectual Property (IP) in the form of 3D design and printing parameters. We introduce a novel methodology to reverse engineer the thermal images acquired from the thermal side-channel to extract specific information (such as speed, temperature, axis of movement, etc.) present in the cyber-domain. To the best of our knowledge, this kind of forensics has not yet been explored in additive manufacturing systems.

Security is one of the major challenges for Cyber-Physical Systems (CPS) design. Identifying flaws as early as possible in the CPS design saves time and money [6]; between 5× to 10× less expensive [7] than finding them during the detailed design stages. This paper makes a case for finding cybersecurity flaws as early as possible. Not only for the temporal and cost benefits, but more importantly, for the integrity of the system once in operation. We introduce a security-aware functional modeling methodology, supported by simulation to validate the robustness of the system in the presence of attacks and countermeasures. Our ideas are implemented in a design automation tool in Amesim and Matlab/Simulink. We use an automotive use-case as an example to validate the methodology and the tool.

Bridging disparate realms of physical and cyber system components requires models and methods that enable rapid evaluation of design alternatives in cyber-physical systems (CPS). The diverse intellectual traditions of physical and mathematical sciences makes this task exceptionally hard. This paper seeks to explore potential solutions by examining specific examples of CPS applications in automobiles and smart buildings. Both smart buildings and automobiles are complex systems with embedded knowledge across several domains. We present our experiences with development of CPS applications to illustrate the challenges that arise when expertise across domains is integrated into the system, and show that creation of models, abstractions, and architectures that address these challenges are key to next generation CPS applications.

This paper discusses an economically profitable way to deploy a residential microgrid incorporating a new market entity “residential aggregator” between the prosumers1 and the Utility. In this residential microgrid, the residential aggregators will have better negotiating capabilities (e.g. in the DR programs) in the electricity market and therefore will be able to bring economic advantages to all participating stakeholders (prosumers, Utility, and aggregator). However, to implement such a microgrid, various rules regarding electricity pricing will need to be put in place. This paper highlights such rules and their impacts, and two example use cases are used to show the different types of distributed energy resources that would be required for profitable residential microgrid deployment.

Residential microgrid has the capability to participate in the distribution grid as a very flexible and dynamic component for demand side energy management (energy efficiency, peak-load reduction, and demand response). Various hierarchical (appliance, home, and neighborhood level) advanced control algorithms need to be developed and validated for such residential microgrids. GridLAB-D is the most promising tool for power system modeling of a microgrid. However, it is limited in supporting advanced control algorithm development with debugging support and does not provide a user friendly interface for modeling the structural and behavioral aspects of a residential microgrid. Therefore, in this paper, we present a new Matlab toolbox (GridMat) to integrate the capabilities of domain-specific modeling & simulation tools from power system (GridLAB-D) and control (Matlab). The GridMat tool supports user friendly model creation, robust debugging, and intelligent grid impact analysis utilities. To demonstrate the capability of GridMat, we have implemented three different levels of energy management controllers (including direct load control) for a residential microgrid using this tool to reduce and shift peak load according to Time-Of-Use (TOU) electricity rate.

Cyber-Physical Energy Systems (CPES) are an amalgamation of both power gird technology, and the intelligent communication and co-ordination between the supply and the demand side through distributed embedded computing. Through this combination, CPES are intended to deliver power efficiently, reliably, and economically. The design and development work needed to either implement a new power grid network or upgrade a traditional power grid to a CPES-compliant one is both challenging and time consuming due to the heterogeneous nature of the associated components/subsystems. The Model Based Design (MBD) methodology has been widely seen as a promising solution to address the associated design challenges of creating a CPES. In this paper, we demonstrate a MBD method and its associated tool for the purpose of designing and validating various control algorithms for a residential microgrid. Our presented co-simulation engine GridMat is a MATLAB/Simulink toolbox; the purpose of it is to co-simulate the power systems modeled in GridLAB-D as well as the control algorithms that are modeled in Simulink. We have presented various use cases to demonstrate how different levels of control algorithms may be developed, simulated, debugged, and analyzed by using our GridMat toolbox for a residential mi-crogrid.

The high complexity of cross-domain engineering in combination with the pressure for product innovation, higher quality, time-to-market, and budget constraints make it imperative for companies to use integrated engineering methods and tools. Computer engineering tools are mainly focused on a particular domain and therefore it is difficult to combine different tools for system-level analysis. This paper presents a novel approach and tool for integrated cyber-physical systems (CPS) design based on the FBS (Function-Behavior-State) methodology where multi-domain simulation models capturing both the behavioral-structural aspects of a system are automatically generated from its functional description. Our approach focuses on simulation-enabled FBS models using automatic and context - sensitive mappings of standard Functional Basis elementary functions to simulation components described in physical modeling languages (i.e. Modelica). Using a real electromechanical CPS application we demonstrate how our context-sensitive synthesis approach generates industry-quality executable functional models of higher quality than state-of-the-art approaches using manual mapping.

The amount of power that can be provided for charging the batteries of the electric vehicles connected to a single neighborhood step-down transformer is constrained by the infrastructure. This paper presents a distributed and collaborative residential-level power grid management application to alleviate the need of costly infrastructure upgrade. The application is designed to be hosted in our in-house developed network-as-automation platform (NAP) technology where most of the control functionalities may be moved onto the networking devices. Moreover, we have adapted a service-oriented software engineering principle to achieve scalability, autonomous, and architecture agnostic properties for the residential-level EV charging. We demonstrate a functional prototype where off-the-shelf networking devices capable to host a Linux Operating system are used to showcase the NAP technology. Furthermore, we developed a web-based user interface that may be accessible from any standard computing device, e.g. iPhone, to monitor the runtime operation of this application.

For decades the semiconductor industry has been driven by Moore's Law and performed aggressive technology scaling to achieve low-power and high-performance. Meanwhile, the semiconductor industry has faced severe reliability challenges like soft-error. Many methodologies (such as redundancy methodologies) have been proposed to improve the soft-error reliability of GPU based systems. However, the GPU compiler has yet to be considered for improving the soft-error reliability of the GPU. In this paper, we propose a novel GPU architecture-aware compilation methodology to further improve the soft-error reliability. The proposed methodology jointly considers the parallel behavior of the GPU and the applications and minimizes the vulnerability of the GPU applications during instruction scheduling. The experimental results show that our methodology is able to perform the scheduling within 5.88 seconds on average and achieves soft-error reliability improvement up to 40% compared to the state-of-the-art compilation techniques. The results show that the performance and power overheads of our methodology are less than 10% in most of the cases.

Part 2: System-Level Design; International audience; Hardware/software codesigns are often modeled with the system level design language SystemC. Especially for safety critical applications, it is crucial to guarantee that such a design meets its requirement. In this paper, we present an approach to formally verify SystemC designs using the UCLID satisfiability modulo theories (SMT) solver. UCLID supports finite precision bitvector arithmetics. Thus, we can handle SystemC designs on a bit-precise level, which enables us to formally verify deeply integrated hardware/software systems that comprise detailed hardware models. At the same time, we exploit UCLID’s ability to handle symbolic variables and use k-inductive invariant checking for SystemC designs. With this inductive approach, we can counteract the state space explosion problem, which model checking approaches suffer from. We demonstrate the practical applicability of our approach with a SystemC design that comprises a bit- and cycle-accurate model of a UART and software that reads data from the UART.

Part 2: System-Level Design; International audience; MATLAB/Simulink is a widely-used industrial tool for the development of complex embedded systems. However, due to the complexity and the dynamic character of the developed models, their analysis is a difficult challenge, in particular if timing aspects are involved. In this paper, we present an approach for the construction of timed path conditions for MATLAB/Simulink models. Timed path conditions allow for fine-grained conclusions about the existence of possibly critical paths through a model containing time-dependent elements. With the help of timed path conditions, it is possible to identify interference and non-interference between model parts. Furthermore, they have the potential to reduce the complexity of models to improve verifiability, reason about compliance with security policies as well as generate feasible, efficient test cases. We demonstrate the applicability of our approach with a shared buffer for public as well as confidential data.