The growing complexity of cyber-physical systems (CPSs) calls for early prototyping tools that combine accuracy, speed, and usability. Virtual Platforms (VPs) provide fast functional simulation, but hybrid co-emulation solutions, in which key digital components are deployed on FPGA, become necessary when accurate timing modelling is required and RTL simulation is too costly. However, existing hybrid emulation tools are mostly proprietary, and rely on vendor-specific FPGA features. To address this gap, we introduce an open-source framework that connects SystemC-based VPs with FPGA emulation, enabling full-system co-emulation of digital and non-digital components. The FPGA accelerates the execution of main digital subsystems, while a wrapper coordinates timing and communication with the VP through JTAG, maintaining synchronization with simulated peripherals. Evaluations using a RISC-V SoC, with an example in the biosignals processing domain, show up to 2500× speedup compared to RTL simulation, while maintaining less than 2× total simulation time relative to pure FPGA emulation.

The growing demand for power to support new cloud services raises the question of how to power future data center infrastructures. A power microgrid and cloud simulator that can act as a unified digital twin of these new infrastructures is crucial for studying emerging scenarios. In this article, we propose Artemis, a co-simulation environment for power microgrids and cloud data centers. Artemis extends the combination of the CloudSim Plus simulator and the Amethyst virtual machine allocation and migration policy with a generalized power microgrid model. Ultimately, Artemis enables the study of modular power microgrids with custom electrical policies and returns performance metrics and visualizations of the data center’s status under observation.

Ensuring both electrical performance and effective thermal management in large-scale battery packs is a critical challenge for next-generation electric mobility and energy storage systems. Current modeling approaches often rely on rigid configurations or computationally expensive CFD simulations, limiting their use in early design stages. This work introduces a modular, compositional framework that enables the dynamic construction of battery packs of arbitrary size, where each cell is modeled individually with coupled electrical and thermal dynamics. The framework integrates a configurable liquid cooling system supporting multiple layouts and coolant types, allowing rapid evaluation of thermal management strategies under diverse operating conditions. By combining scalability, flexibility, and high computational efficiency, the proposed approach accelerates design iterations, reduces prototyping costs, and supports the development of safer and more reliable battery systems for real-world applications.

Photoplethysmography-based Blood Pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is desirable to ensure user data confidentiality. Recent Deep Neural Networks (DNNs) have achieved high BP estimation accuracy by reconstructing BP waveforms or directly regressing BP values, but their large memory, computation, and energy requirements hinder deployment on wearables. This work introduces a fully automated DNN design pipeline that combines hardware-aware Neural Architecture Search, pruning, and Mixed-Precision Search to generate accurate yet compact BP prediction models optimized for ultra-low-power multi-core Systems-on-Chip (SoCs). Starting from state-of-the-art baseline models on four public datasets, our optimized networks achieve up to 7.99% lower error with a 7.5 \(\times\) parameter reduction, or up to 83 \(\times\) fewer parameters with negligible accuracy loss. All models fit within 512 kB of memory on our target SoC (GreenWaves’ GAP8), requiring less than 55 kB and achieving an average inference latency of 142 ms and energy consumption of 7.25 mJ. Patient-specific fine-tuning further improves accuracy by up to 64%, enabling fully autonomous, low-cost BP monitoring on wearables.

The TRISTAN project aims to expand and industrialize the European RISC-V ecosystem to compete effectively with existing commercial alternatives. This initiative specifically targets the critical challenges in the development of Electronic Design Automation (EDA) tools, essential for RISC-V-based solutions, by leveraging the synergy between the open-source community and industrial solutions. This paper presents an overview of the current landscape of TRISTAN's EDA flow, highlighting specific tools and methodologies that streamline the early design phases of RISC-V-based systems. We explore the unique features of these tools, emphasizing how they complement each other to strengthen the overall design process.

Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improving the system's lifetime. Most existing estimation methods are either physics-based digital twins of the battery or data-driven models such as Neural Networks (NNs). In this work, we propose two new contributions in this domain. First, we introduce a novel NN architecture formed by two cascaded branches: one to predict the current SoC based on sensor readings, and one to estimate the SoC at a future time as a function of the load behavior. Second, we integrate battery dynamics equations into the training of our NN, merging the physics-based and data-driven approaches, to improve the models' generalization over variable prediction horizons. We validate our approach on two publicly accessible datasets, showing that our Physics-Informed Neural Networks (PINNs) outperform purely data-driven ones while also obtaining superior prediction accuracy with a smaller architecture with respect to the state-of-the-art.

Batteries are a central component of many complex systems, including mobile devices, sensors, electric vehicles, etc. Keeping the battery working in normal conditions avoids dangerous hazards for the user or the system itself and helps extend the device’s life. The battery temperature is one of the most delicate aspects of these devices since some dangerous scenarios, like thermal runaway, could occur due to variable conditions. This paper uses a battery model of an electric vehicle from the automotive area as a case study to simulate the thermal response to normal usage. Then, thermal fault scenarios are modeled within equivalent circuital device descriptions and analyzed regarding state-of-charge, temperature, and voltage output. The findings presented offer a valuable starting point for improving the design phase of the batteries in multiple fields by testing fault scenarios already during simulation.

The recent advancements of the automotive sector demand robust co-simulation methodologies that enable early validation and seamless integration across hardware and software domains. However, the lack of standardized interfaces and the dominance of proprietary simulation platforms pose significant challenges to collaboration, scalability, and IP protection. To address these limitations, this paper presents an approach for automatically wrapping SystemC models by using the Functional Mock-up Interface (FMI) standard. This method combines the modeling accuracy and fast time-to-market of SystemC with the interoperability and encapsulation benefits of FMI, enabling secure and portable integration of embedded components into co-simulation workflows. We validate the proposed methodology on real-world case studies, demonstrating its effectiveness with complex designs.

Virtual Platforms (VPs) enable early software validation of autonomous systems’ electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies.

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.

With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300k€ academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.

Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run-time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improve-ment. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.

Under the pressure of the Industry 4.0 revolution, and now with the European Chips Act, smart systems are becoming omnipresent in all industrial sectors, e.g., automotive and aerospace. Such systems contain digital and analog components belonging to several physical domains, e.g., electrical and mechanical. To ensure robustness, the whole system must be validated as early as possible in the development cycle, by taking into account all such domains, as recommended by the ISO 26262 standard in the case, e.g., of automotive systems. Unfortunately, validation techniques, including fault injection and simulation are not as advanced on the analog side as the digital counterpart: i) they are not fully standardized ii) they are highly domain-dependent, and iii) they are performed separately from the digital flow. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios, it is possible to improve the robustness of the analog part and, at the same time, to improve the quality of the digital part that controls the system functionality. A multi-domain case study containing a microcontroller and a three-axis accelerometer is presented to demonstrate the validity of the proposed approach in many industrial contexts.

Smart systems contain digital and analog components of several physical domains, e.g., electrical and mechanical During the design phase, the fault injection, which checks the system functionality following the guidelines of ISO standard 26262, enhances the system’s robustness. Unfortunately, fault injection and simulation on the analog side are i) not fully standardized compared to their digital counterparts, ii) highly domain-dependent, and iii) performed separately from the digital. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios it is possible to improve the robustness of the analog part and simultaneously improve the quality of the digital part that controls the system functionality.

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer.

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.

Constructing a holistic digital twin of a system composed of multiple physical domains is crucial for various tasks. In particular, when the simulation is extended with faults, it becomes a very important resource to achieve robust functional safety analysis. This article proposes a new methodology to build non-electrical fault models for the thermal domain. Such thermal faults are defined through an electrical circuit representing the thermal behavior of the system, known as the Cauer network, based on the physical analogies between the two domains. Including this thermal representation in a multi-domain system allows to simulate the interconnections between different physical domains, thus achieving a more realistic system behavior and evaluating the mutual impact of different domains (e.g., mechanical, electrical and thermal). The entire methodology is applied to a complex case of study implemented by using Verilog-AMS as a proof of concept.

Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several data-driven SOH models have been proposed, whose accuracy heavily relies on the quality of the datasets used for their training. Since these datasets are obtained from measurements, they are limited in the variety of the charge/discharge profiles. To address this scarcity issue, we propose generating datasets by simulating a traditional battery model (e.g., a circuit-equivalent one). The primary advantage of this approach is the ability to use a simulatable battery model to evaluate a potentially infinite number of workload profiles for training the data-driven model. Furthermore, this general concept can be applied using any simulatable battery model, providing a fine spectrum of accuracy/complexity tradeoffs. Our results indicate that using simulated data achieves reasonable accuracy in SOH estimation, with a 7.2 % error relative to the simulated model, in exchange for a 27X memory reduction and a$\approx 2000\mathrm{X}$speedup.

The heterogeneous nature of SystemC-AMS makes it a perfect candidate solution to support Cyber-Physical Production Systems (CPPSs), i.e., systems that are characterized by a tight interaction of the cyber part with the surrounding physical world and with manufacturing production processes. Nonetheless, the support for the modeling of physical and mechanical dynamics typical of production machinery goes far beyond the initial application scenario of SystemC-AMS, thus limiting its effectiveness and adoption in the production and manufacturing context. This paper starts with an analysis of the current adoption of SystemC-AMS to highlight the open points that still limit its effectiveness, with the goal of pinpointing current issues and to propose solutions that could improve its effectiveness, and make SystemC-AMS an essential resource also in the new Industry 4.0 scenario.

PhotoVoltaic (PV) installations are a widespread source of renewable energy, and are quite common urban buildings’ roofs. To soften both the initial investment and the recurrent maintenance costs, the current market trends delegate the construction of PV installations to Energy Aggregators, i.e., grouping of consumers and producers that act as a single entity to satisfy local energy demand and to sell the surplus energy to the grid. In this perspective, PV installations can be designed with a larger perspective, i.e., at district level, to maximize power production not of a single building but rather of a number of blocks of a city. This implies new challenges, including efficient data management (the covered area can be squared kilometers wide) and optimal PV installation (the number of PV modules can be in the order of hundreds or even thousands). This paper proposes a framework to combine detailed geographic and irradiance information to determine an optimal PV installation over a district, by maximizing both power production and economic convenience. Our simulation results run on a real-world district prove that the framework allows an advanced evaluation of costs and benefit, that can be used by Energy Aggregators to design a new PV installation, and demonstrate an improvement on power generation up to 20% w.r.t. standard installations.

Digital twins of production lines do not focus solely on the management of the production process, they can also monitor and optimize other extra-functional aspects such as energy consumption and communications. This paper proposes the extension of digital twin concept in such directions. First, we extend the digital twin with models of energy consumption, that allow the monitoring of production line components throughout production lifetime. Then, we propose a flow to design the communication network starting from information obtained from the digital twin concerning the production, usage and flowing of information through the plant. All these methodologies start from the production line specification, then they enrich it with data collected during operation, and finally information is used to perform design and optimization. Results have been shown on a real Industry 4.0 research facility.

One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market. Nonetheless, the lack of repeatability of the manufacturing process, which typically translates into a lack of reproducibility and reliability of the quality of the final products compared to traditional subtractive technologies, is currently one of the major barriers to the widespread adoption of AM in mass production. To overcome this limitation, there are growing efforts in recent years toward better integration of advanced information technologies into AM, exploiting the layer-by-layer nature of the build. The consequence of these efforts is twofold: 1) the integration of advanced sensing technologies into the AM systems, making possible the in situ monitoring of huge amounts of data at multiple time scales and resolutions and 2) the ever-increasing role of data-driven approaches [especially machine learning (ML)] in the analysis of such data to provide real-time quality monitoring and process optimization. This article introduces and reviews the key technological developments of this phenomenon, with a special focus on metal powder bed fusion (PBF) technologies that are attracting the highest attention by the industrial AM community. After introducing the main manufacturing quality issues and needs that have to be developed and optimized, we provide a wide overview of the latest progress of in situ monitoring and control in metal PBF, with special regards to sensing technologies and ML approaches. Finally, we identify the open challenges and future research directions in this field.

The design of photovoltaic (PV) installations mostly relies on rule-of-thumb criteria and on gross estimates of the shading patterns, and the few optimized approaches are generally focused on the problem of identifying the most suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This article proposes a framework to address the design and the optimization of PV installations through a set of microservices focusing on the different variables of the design: identification of the target surfaces, elaboration of weather data, modeling of the PV panel, and floorplanning of the panel on the surface. The microservices architecture ensures extensibility and generality, as the user may execute only a subset of the proposed services or provide novel algorithms to extend the existing ones. Additionally, the framework provides a set of built-in models that allow sensitivity to the distribution of shades and accurate modeling of the power production over time. We show the many benefits of the proposed framework on two different use cases.

Following the Smart Grid view, current energy generation systems based on fossil fuels will be replaced with renewable energy sources. Photovoltaic (PV) is currently considered the most promising technology, due to decreasing costs of the devices and to the limited invasiveness in existing infrastructures, that make PV installations quite common urban buildings' roofs. To maximise both power production and Return Of Investment (ROI) of PV installations, new techniques and methodologies should be applied to limit sources of inefficiencies, like shading and power losses due to an incorrect installation. In this paper, we propose a novel solution for an optimal configuration and placement of PV systems in buildings' roofs. Given a number of alternative configurations and a roof of interest, it combines detailed geographic and irradiance information to determine the optimal PV installation, by maximizing both power production and ROI. Our simulation results on two real-world roofs demonstrate an improvement on power generation up to 23% w.r.t. standard compact installations. These results also highlight that a cost analysis, often ignored by standard installation strategies, is nonetheless necessary to guarantee optimal results in terms of PV production and revenue.

Given the computational complexity of Recurrent Neural Networks (RNNs) inference, IoT and mobile devices typically offload this task to the cloud. However, the execution time and energy consumption of RNN inference strongly depends on the length of the processed input. Therefore, considering also communication costs, it may be more convenient to process short input sequences locally and only offload long ones to the cloud. In this paper, we propose a low-overhead runtime tool that performs this choice automatically. Results based on real edge and cloud devices show that our method is able to simultaneously reduce the total execution time and energy consumption of the system compared to solutions that run RNN inference fully locally or fully in the cloud.

Semi-empirical models of photovoltaic (PV) modules based only on datasheet information are popular in electrical energy systems (EES) simulation because they can be built without measurements and allow quick exploration of alternative devices. One key limitation of these models, however, is the fact that they cannot model the presence of bypass diodes, which are inserted across a set of series-connected cells in a PV module to mitigate the impact of partial shading; datasheet information refer in fact to the operations of the module under uniform irradiance. Neglecting the effect of bypass diodes may incur in significant underestimation of the extracted power.This paper proposes a semi-empirical model for a PV module, that, by taking into account the only available information about bypass diodes in a datasheet, i.e., its number, by a first downscaling the model to a single PV cell and a subsequent upscaling to the level of a substring and of a module, allows to take into accout the diode effect as much accurately as allowed by the datasheet information.Experimental results show that, in a typical PV array on a roof, using a diode-agnostic model can signifantly underestimate the output power production.

In the context of industry 4.0, it is strategic to support factories with innovative maintenance approaches, so to avoid faults and decrease the risks of a production stop. The first step of the digitization of factories has been the collection of large amounts of data monitoring the health status of the plant. However, such data is of little use unless it is clearly correlated with information about faults occurred on the line: some faults may be sporadic, or happen only in extremely critical conditions, and thus no data may be available related to their occurrence. Artificially generating such data would force to actually damage the plant, that is of course not a viable solution. The goal of this work is to generate faulty temporal series, that reproduce the behavior of a component on the occurrence of specific faults. The innovative approach models the component of interest in Verilog-AMS (VAMS) and systematically injects the faults of interest, by keeping a direct link with the real possible cause of such faulty behavior on the plant. To prove the effectiveness of the proposed solution, the approach is applied to a direct current motor (DC motor), an electromechanical system that converts electrical energy into mechanical energy.

Modern cyber-physical electrical energy systems (CPEES) are characterized by wider adoption of sustainable energy sources and by an increased attention to optimization, with the goal of reducing pollution and wastes. This imposes a need for instruments supporting the design flow, to simulate and validate the behavior of system components and to apply additional optimization and exploration steps. Additionally, each system might be tested with a number of management policies, to evaluate their economic impact. It is thus evident that simulation is a key ingredient in the design flow of CPEES. This paper proposes a framework for CPEES modeling and simulation, that relies on the open-source standard SystemC-AMS. The paper formalizes the information and energy flow in a generic CPEES, by focusing on both AC and DC components, and by including support for mechanical and physical models that represent multiple energy sources and loads. Experimental results, applied to a complex CPEES case study, will prove the effectiveness of the proposed solution, in terms of accuracy, speed up w.r.t. the current state-of-the-art Matlab/Simulink, and support for the design flow.

Smart meters communicate to the utility provider fine-grain information about a user's energy consumption, which could be used to infer the user's habits and pose thus a critical privacy risk. State-of-the-art solutions try to obfuscate the readings of a meter either by using a large re-chargeable battery to filter the trace or by adding random noise to alter it. Both solutions, however, have significant drawbacks: large batteries are prohibitively expensive, whereas digitally added noise implies that the user entrusts the utility provider to protect his/her privacy.

Adaptive reconfiguration of a photo-voltaic (PV) panel by means of a switch network is a well-known approach to tackle shading issues dynamically and with a reasonable cost. Most of these approaches assume however that the entire panel is reconfigurable, resulting in high installation costs due to the large wiring overhead required by this solution. In this work we propose an architecture in which only a portion of the panel is made reconfigurable, while minimizing the loss in the extracted power with respect to a fully reconfigurable solution. The key feature of our approach is the use of environmental (irradiance and temperature) data to determine the reconfigurable subset at design time. Simulation results show that, by reconfiguring only about 50-70% of a panel, it is possible to achieve up to 45% power increase with respect to a static topology, while losing less than 5% power with respect to full reconfiguration.

Virtual platforms are a powerful support for the development and early validation of embedded SW. However, complex smart devices are built by aggregating heterogeneous components provided by different vendors, thus requiring the development of custom ad-hoc virtual platforms. Even worse, components of the underneath HW platform may belong to different design domains, that are usually expressed using a huge variety of different languages. This high degree of heterogeneity in terms of both design and specification languages must be effectively managed by the design flow so to help engineers in assembling the final system. This paper proposes a meet-in-the-middle approach to create virtual platforms of heterogeneous systems. The starting point is a set of heterogeneous models, developed by adopting the designer's favorite language and formalism. The methodology envisions the adoption of existing automatic translation and abstraction tools to automatically integrate models of components into a single homogeneous system-level executable description. The approach is supported by an analysis of the typical design flow, that leads to the definition of design domain/abstraction level taxonomies. Such taxonomies are then used to identify what characteristics would allow efficient system-level simulation, and the corresponding transformations to be applied to the starting models to achieve a “holistic” system executable representation. The benefit of such an approach is particularly evident on any kind of highly heterogeneous systems, such as smart devices. The proposed methodology has been applied to two case studies with different degrees of heterogeneity, with the goal exemplifying its adoption on concrete scenarios and to prove its effectiveness.

Temperature is a critical property of smart systems, due to its impact on reliability and to its inter-dependence with power consumption. Unfortunately, the current design flows evaluate thermal evolution ex-post on offline power traces. This does not allow to consider temperature as a dimension in the design loop, and it misses all the complex inter-dependencies with design choices and power evolution. In this article, by adopting the functional language SystemC-AMS (Analog Mixed Signal), we propose a method to enable thermal/power/functional co-simulation. The system thermal model is built by using state-of-the-art circuit equivalent models, by exploiting the support for electrical linear networks intrinsic of SystemC-AMS. The experimental results will show that the choice of SystemC-AMS is a winning strategy for building a simultaneous simulation of multiple functional and extra-functional properties of a system. The generated code exposes an accuracy comparable to that of the reference thermal simulator HotSpot. Additionally, the initial overhead due to the general purpose nature of SystemC-AMS is compensated by the surprisingly high performance of transient simulation, with speedups as high as two orders of magnitude.

Smart systems are characterized by the integration in a single device of multi-domain subsystems of different technological domains, namely, analog, digital, discrete and power devices, MEMS, and power sources. Such challenges, emerging from the heterogeneous nature of the whole system, combined with the traditional challenges of digital design, directly impact on performance and on propagation delay of digital components. This article proposes a design approach to enhance the RTL model of a given digital component for the integration in smart systems with the automatic insertion of delay sensors, which can detect and correct timing failures. The article then proposes a methodology to verify such added features at system level. The augmented model is abstracted to SystemC TLM, which is automatically injected with mutants (i.e., code mutations) to emulate delays and timing failures. The resulting TLM model is finally simulated to identify timing failures and to verify the correctness of the inserted delay monitors. Experimental results demonstrate the applicability of the proposed design and verification methodology, thanks to an efficient sensor-aware abstraction methodology, by applying the flow to three complex case studies.

Shading is a crucial issue for the placement of PV installations, as it heavily impacts power production and the corresponding return of investment. Nonetheless, residential rooftop installations still rely on rule-of-thumb criteria and on gross estimates of the shading patterns, while more optimized approaches focus solely on the identification of suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work addresses the challenge of identifying an optimal (with respect to the overall energy production) placement of PV panels on a roof. The novel aspect of the proposed solution lies in the possibility of having a sparse, irregular placement of individual modules so as to better exploit the variance of solar data. The latter are represented in terms of the distribution of irradiance and temperature values over the roof, as elaborated from historical traces and Geographical Information System (GIS) data. Experimental results will prove the effectiveness of the algorithm through three real world case studies, and that the generated optimal solutions allow to increase power production by up to 28% with respect to rule-of-thumb solutions.

Despite of being one of the most widespread green energy sources, the efficiency of PV rooftop installations is still repressed by shading and by the absence of a rigorous irradiance-aware placement approach. The goal of this work is to reach optimal energy production via an irregular placement of PV modules, by considering two degrees of freedom: orientation of each PV module and topology. Experimental results will prove the effectiveness of the proposed solution onto two real world case studies, with an increase of power production of up to 40%.

One of the ambitious goals of the "Smart city" paradigm is to design zero-energy buildings. Buildings can be considered as connected cyber-physical systems that require the construction of sound methodologies inherited from the Electronic Design Automation (EDA) research. In particular, aiming at autonomous buildings, the effective design of renewable energy sources is a key aspect for which such methodologies have to be developed. In this work, we propose a modeling strategy for the early estimation of the performance of photovoltaic (PV) arrays. Although a plethora of PV panel models there exists, most of these models suffer from accuracy/complexity tradeoffs. On one hand, building fast models forces to ignore either the correlation between temperature and irradiance, or the topology of panels, thus yielding inaccurate estimations. On the other, more accurate models are time consuming and require costly measurements or circuit analysis, that cannot be extracted from the sole datasheet. This paper proposes a compact semi-empirical model, suitable for real time simulation and built solely from information derived from the PV panel datasheet. The model is built by empirically fitting an expression of the panel operating point as a function of both irradiance and temperature, and of the adopted PV system topology. The accuracy and effectiveness of the proposed model have been validated w.r.t. the production traces of the PV systems of a real world industrial building.

Analog components are fundamental blocks of smart systems, as they allow a tight interaction with the environment, in terms of both sensing/actuation and communication. This impacts on the design of the overall system, and mainly on the validation phase, that thus requires the joint simulation of digital and analog aspects. In this scenario, this paper proposes the automatic conversion of analog models to C++-based languages, to remove the overhead of co-simulation with traditional virtual platform tools. The proposed methodology allows to convert a given analog description to either: 1) a fully equivalent description or 2) an abstract representation for faster simulation which models only the aspects of interest. Effectiveness and correctness have been proved on a number of case studies that highlight the effectiveness and potentiality of the proposed methodology.

Smart systems represent a broad class of intelligent, miniaturized devices incorporating functionality like sensing, actuation, and control. In order to support these functions, they must include sophisticated and heterogeneous components, such as sensors and actuators, multiple power sources and storage devices, digital signal processing, and wireless connectivity. The high degree of heterogeneity typical of smart systems has a heavy impact on their design: the challenges are not in fact restricted to their functionality, but are also related to a number of extra-functional properties, including power consumption, temperature, and aging. Current simulation- or model-based design approaches do not target a smart system as a whole, but rather single domains (digital, analog, power devices, etc.) or properties. This paper tries to overcome this limitation by proposing a framework for the concurrent simulation of both functionality and such extra-functional properties. The latter are modeled as different information flows, managed by dedicated “virtual buses” and formalized through the adoption of IP-XACT. SystemC, through the support of physical and continuous time modeling provided by its analog and mixed signal extension, is used to implement both functional and extra-functional models. Experimental results show the efficiency, accuracy and modularity of the proposed approach on an example case study, in which substantial speedups with respect to standard model-based design tools go along with a very high degree of accuracy (-5%). Furthermore, the case study highlights that the proposed framework allows to easily capture at run time the mutual impact of properties, e.g., in case of power and temperature.

The increasing processing power of today's HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels.

Smart systems are miniaturized devices integrating computation, communication, sensing and actuation. As such, their design can not focus solely on functional behavior, but it must rather take into account different extra-functional concerns, such as power consumption or reliability. Any smart system can thus be modeled through a number of views, each focusing on a specific concern. Such views may exchange information, and they must thus be simulated simultaneously to reproduce mutual influence of the corresponding concerns. This paper shows how the IP-XACT standard, with some necessary extensions, can effectively support this simultaneous simulation. The extended IP-XACT descriptions allow to model extra-functional properties with a homogeneous format, defined by analysing requirements and characteristic of three main concerns, i.e., power, temperature and reliability. The IP-XACT descriptions are then used to automatically generate a skeleton of the simulation infrastructure in SystemC. The skeleton can be easily populated with models available in the literature, thus reaching simultaneous simulation of multiple concerns.

Out of the many options available for thermal simulation of digital electronic systems, those based on solving an RC equivalent circuit of the thermal network are the most popular choice in the EDA community, as they provide a reasonable tradeoff between accuracy and complexity. HotSpot, in particular, has become the de-facto standard in these communities, although other simulators are also popular. These tools have many benefits, but they are relatively inefficient when performing thermal analysis for long simulation times, due to the occurrence of a large number of redundant computations intrinsic in the underlying models.

Models of power sources are essential elements in the simulation of systems that generate, store and manage energy. In spite of the huge difference in power scale, they perform a common function: converting a primary environmental quantity into power. This paper proposes a unified model of a power source that is applicable to any power scale, and that can be derived solely from data contained in the specification or the datasheet of a device. The key feature of our model is the normalization of the energy generation characteristic of the power source by means of a reduction to a function expressing extracted power vs. the "scavenged" quantity. The proposed model proved to apply to two kinds of power sources, i.e., a wind turbine and a photovoltaic panel, and to provide a good level of accuracy and simulation performance w.r.t. widely adopted models.

Simulation speed is crucial in virtual platforms, in order to enhance the design flow with early validation and design space exploration. This work tackles this challenge by focusing on two main techniques for speeding up virtual platform simulation, namely efficient data types implementation and a novel scheduling technique. Both the optimizations are obtained through code manipulation. The target language is C++ and its extensions (i.e., SystemC), that are the most widespread languages for virtual platform modeling and simulation. The optimization techniques are considered orthogonal, as they target different aspects of the simulated code. Experimental results prove the effectiveness of both the single techniques and of their combined application on complex case studies, with the result of reaching a maximum speedup of 70$\times$in the simulation of a virtual platform.

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SystemC has recently been extended with the Analogue and Mixed Signal (AMS) library, with the ultimate goal of providing simulation support to analogue electronics and continuous time behaviours. SystemC-AMS allows modelling of systems that are either conservative and extremely low level or continuous time and behavioural, which is limited compared to other AMS HDLs. This work faces up this challenge, by extending SystemCAMS support to a new level of abstraction, called Analogue Behavioural Modelling (ABM), covering models that are both behavioural and conservative. This leads to a methodology that uses SystemC-AMS constructs in a novel way. Full automation of the methodology allows proof of its effectiveness both in terms of accuracy and simulation performance, and application of the overall approach to a complex industrial Micro Electro- Mechanical System (MEMS) case study. The effectiveness of the proposed approach is further highlighted in the context of virtual platforms for smart systems, as adopting a C++-based language for MEMS simulation reduces the simulation time by about 2x, thus enhancing the design and integration flow.

With the advent of heterogeneous multiprocessor system-on-chips (MPSoCs), hardware/software partitioning is again on the rise both in research and in product development. In this new scenario, implementing intellectual-property (IP) blocks as SW applications rather than dedicated HW is an increasing trend to fully exploit the computation power provided by the MPSoC CPUs. On the other hand, whole libraries of IP blocks are available as RTL descriptions, most of them without a corresponding high-level SW implementation. In this context, this article presents a methodology to automatically generate SW applications in C++, by starting from existing RTL IPs implemented in hardware description language (HDL). The methodology exploits an abstraction algorithm to eliminate implementation details typical of HW descriptions (such as cycle-accurate functionality and data types) to guarantee relevant performance of the generated code. The experimental results show that, in many cases, the C++ code automatically generated in a few seconds with the proposed methodology is as efficient as the corresponding code manually implemented from scratch.

Smart systems implement the leading technology advances in the context of embedded devices. Current design methodologies are not suitable to deal with tightly interacting subsystems of different technological domains, namely analog, digital, discrete and power devices, MEMS and power sources. The effects of interaction between components and with the environment must be modeled and simulated at system level to achieve high performance. Focusing on the digital domain, additional design constraints have to be considered as a result of the integration of multi-domain subsystems in a single device. The main digital design challenges, combined with those emerging from the heterogeneous nature of the whole system, directly impact on performance and on propagation delay of the digital component. This paper proposes a design approach to enhance the RTL model of a given digital component for the integration in smart systems, and a methodology to verify the added features at system-level. The design approach consists of augmenting the RTL model through the automatic insertion of delay sensors, which can detect and correct timing failures. The augmented model is abstracted to SystemC TLM and, then, mutants (i.e., code mutations for emulating timing failures) are automatically injected into the model. Experimental results demonstrate the applicability of the proposed design and verification methodology and the effectiveness of the simulation performance.

Electrical energy systems (EESs) are systems which consume, generate, distribute and store energy at various scales. This paper presents a modeling and simulation framework that uses principles borrowed from the system-level simulation of digital systems and extends them to the case of EESs. The framework relies on open-source standards such as SystemC (and its Analog and Mixed-Signal extensions) for simulation, and IP-XACT for interface definition.

General purpose graphics processing units (GP-GPUs) have recently been explored as a new computing paradigm for accelerating compute-intensive EDA applications. Such massively parallel architectures have been applied in accelerating the simulation of digital designs during several phases of their development - corresponding to different abstraction levels, specifically: (i) gate-level netlist descriptions, (ii) register-transfer level and (iii) transaction-level descriptions. This embedded tutorial presents a comprehensive analysis of the best results obtained by adopting GP-GPUs in all these EDA applications.

Designers are more and more forced to define innovative models and methodologies for managing integration of heterogeneous components and heterogeneous Chip Multiprocessors (CMPs) in modern embedded systems. In this context, component-based design seems the more promising approach, but it suffers from the lack of a widely adopted Model of Computation (MoC) able to capture component heterogeneity. This paper proposes univerCM, a new model of computation based on the Heterogeneous Intermediate Format (HIF) with the aim of supporting bottom-up design and system integration from a set of heterogeneous components. HW and SW components can be described by means of different languages and according to different MoCs, toward a uniform intermediate description based on a rigorous semantics. A mapping from univerCM to SystemC is proposed then to obtain a homogeneous description intended for fast simulation, that can be also used as starting point for CMP design flows. Experimental results show the effectiveness of univerCM in managing system heterogeneity.

IP core integration into an embedded platform implies the implementation of a customized device driver complying with both the IP communication protocol and the CPU organization (single processor, SMP, AMP). Such a close dependence between driver and platform organization makes reuse of already existing device drivers very hard. Designers are forced to manually customize the driver code to any different organization of the target platform. This results in a very time-consuming and error-prone task. In this paper, we propose a methodology to semi-automatically generate customized device drivers, thus allowing a more rapid embedded platform development. The methodology exploits the testbench provided with the RTL IP module for extracting the formal model of the IP communication protocol. Then, a taxonomy of device drivers based on the CPU organization allows the system to determine the characteristics of the target platform and to obtain a template of the device driver code. This requires some manual support to identify the target architecture and to generate the desired device driver functionality. The template is used then to automatically generate drivers compliant with 1) the CPU organization, 2) the use in a simulated or in a real platform, 3) the interrupt support, 4) the operating system, 5) the I/O architecture, and 6) possible parallel execution. The proposed methodology has been successfully tested on a family of embedded platforms with different CPU organizations.

SystemC is a widespread language for HW/SW system simulation and design exploration, and thus a key development platform in embedded system design. However, the growing complexity of SoC designs is having an impact on simulation performance, leading to limited SoC exploration potential, which in turns affects development and verification schedules and time-to-market for new designs. Previous efforts have attempted to parallelize SystemC simulation, targeting both multiprocessors and GPUs. However, for practical designs, those approaches fall far short of satisfactory performance. This paper proposes SAGA, a novel simulation approach that fully exploits the intrinsic parallelism of RTL SystemC descriptions, targeting GPU platforms. By limiting synchronization events with ad-hoc static scheduling and separate independent dataflows, we shows that we can simulate complex SystemC descriptions up to 16 times faster than traditional simulators.

This work proposes an advanced methodology based on an open source virtual prototyping framework for verification of complex Heterogeneous Embedded Systems (HES). It supports early rapid modelling of complex HES through smooth refinements, an open interface based on IP-XACT extensions for secure composition of HES components, and automatic testbench generation over different abstraction levels.

Heterogeneous multicore system-on-chips (MPSoCs) provide many degrees of freedom to map functionalities on either SW and HW components. In this scenario, enabling the remapping of HW IPs as SW routines allows to fully exploit the computation power and flexibility provided by heterogeneous MPSoCs. On the other hand, reuse of existent IP cores is the key strategy to explore this large design space in a reasonable amount of time and to reduce the error risk during the MPSoC design flow. A methodology for automatic generation of parallel SW code taking into account these aspects is currently missing. This paper aims at overcoming this limitation, by presenting a methodology to automatically generate parallel SW IPs starting from existent RTL IP models.

HW-SW partitioning is a key problem in HW-SW code sign of embedded systems studied extensively in the past. All proposed approaches are top down flows, that start from a homogeneous formal specification of the system and determine an optimal partitioning. Thus, the proposed techniques do not exploit reuse nor reconsider the HWSW partitioning of an already designed platform. This paper proposes an extension of traditional flows that allows reuse and automatic generation of components and interfaces. The final flow has been applied to a complex industrial platform to prove the effectiveness and the advantages of the proposed approach.

The generation of device drivers is a very time consuming and error prone activity. All the strategies proposed up to now to simplify this operation require a manual, even formal, specification of the device driver functionalities. In the system-level design, IP functionalities are tested by using testbenches, implemented to contain the communication protocols to correctly interact with the device. The aim of this paper is to present a methodology to automatically generate device drivers from the testbench of any RTL IP. The only manual step required is to tag the states corresponding to the different device functionalities. The Extended Finite State Machines (EFSMs) are then used to create a correct-by-construction two-level device driver: the lower level deals with architectural choices, while the higher one is derived from the EFSMs and it implements the communication protocols. The effectiveness of this methodology has been proved by applying it to a platform provided by STMicroelectronics.

The literature does not report a complete design methodology for WSN applications integrating all these aspects. The proposed methodology allows programmers to write WSN applications by using the system description language SystemC and the Abstract Middleware Environment (AME) framework for fast sim