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.

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.

The construction of control barrier functions (CBFs) based on differentiable optimization ensures the invariance of dynamic systems, which is suitable for collision-free motion planning of robotic manipulators. Yet this class of differentiable optimization algorithms suffers from relatively low calculation performance, especially in scenarios with diverse and complex obstacles. This paper proposes a CBF controller based on an improved differentiable optimization for efficient reactive motion planning of robots. Specifically, the safety control problem for robots is formulated as a quadratic programming problem subject to CBF constraint, referring to CBF and its gradient. Then, the minimum non-negative distance is introduced to construct the differentiable CBFs, which is essentially a convex optimal problem, and can be solved through Gilbert-Johnson–Keerthi (GJK) algorithm to enhance real-time performance. In the mean-time, the gradient of CBF can be calculated by the equivalence between the convex optimal problem and Karush–Kuhn–Tucker (KKT) conditions, which uses the block matrix inverse technique together with Schur complement to compute the inverse of KKT differential matrix and the resultant differential of CBF. Further, the CBF-based quadratic program is employed to derive the control law for efficient motion planning of robots. It is shown that the combination of GJK algorithm and KKT conditions reduces the computational cost of the proposed differential optimization algorithm for motion planning of robots. Finally, simulation studies and comparisons are conducted to validate the effectiveness and superiority of the proposed method for safety control of mobile robots and manipulators over the baseline, particularly in environments with complex-shaped obstacles.

Digital Low Dropout Regulators (LDOs) are an excellent candidate for area-efficient fine-grain power management in heterogeneous systems, leveraging integrated power switches. Relocating the power switches to the backside of the wafer in conjunction with the Backside Power Delivery Network (BSPDN) layer is envisaged as a System Technology Co-Optimization (STCO) booster for finer grain power management and reduced area/cost. We perform a detailed thermal analysis using power-switch-based LDOs enabling per-core DVFS for a high-performance server 3D computing chiplet in a Nanosheet CMOS (A10) technology node with BSPDN. While BSPDN introduces thermal penalties due to a lack of lateral heat spreading, our high-resolution thermal simulations explore the feasibility of moving LDOs to the backside. Increasing the LDO area from 5% to 50% of the backside die area effectively lowers the 2.5/3D System-in-Package (SiP) peak temperature, confirming that thermal concerns do not impede backside LDO integration. This study supports the cost-effective design of next-generation SiPs by demonstrating no adverse thermal impact for relocating power switches to the wafer backside in the nanosheet era.

Indoor positioning is crucial for the effective use of drones in smart environments, enabling precise navigation and control in complex indoor spaces where GPS signals are weak or unavailable and wireless communication-based systems must be used. In order to improve positioning accuracy, various distance measurement techniques and related error correction methods have been proposed in the literature. However, these methods are mostly focused on accuracy and often require a significant amount of computational resources, which is quite inefficient when deployed on battery-operated devices like small robots or drones because of their limited battery capacity. Moreover, conventional error correction methods are little effective for the tracking of moving objects. In this paper, we first analyze the trade-off between energy consumption and accuracy for the error correction and identify the most energy-efficient error correction method. Based on this analysis in the accuracy/energy space, we introduce a new energy-efficient error correction method that is especially targeted for tracking a moving object. We validated our solution by implementing an Ultra-Wideband based indoor positioning system and demonstrated that the proposed method improves positioning accuracy by 15% and reduces energy consumption by 33% compared to the state-of-the-art method.

The demand for high-capacity and energy-efficient memory solutions has surged in the era of data-centric computing, particularly for Artificial Intelligence (AI) and Machine Learning (ML) workloads. This paper introduces a novel memory architecture leveraging Charge-Coupled Device (CCD) technology, engineered in a sequential-access block memory configuration, to enhance Compute-near-Memory (CnM) systems. We propose an optimized 3D IGZO CCD block memory as an on-chip weight buffer for high-capacity CnM systems. Our approach achieves 2.95−131.26× improvement in area efficiency and 1.32−4.33× improvement in energy efficiency compared to SRAM solutions.

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.

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.

Modern smartwatches often include photoplethysmographic (PPG) sensors to measure heartbeats or blood pressure through complex algorithms that fuse PPG data with other signals. In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance of heart rate (HR) tracking while also maximizing the smartwatch's battery life. In particular, we first analyze the trade-offs between running on-device HR tracking or offloading the work to the mobile. Then, thanks to an additional step to evaluate the difficulty of the upcoming HR prediction, we demonstrate that we can smartly manage the workload between smartwatch and smartphone, maintaining a low mean absolute error (MAE) while reducing energy consumption. We benchmark our approach on a custom smartwatch prototype, including the STM32WB55 MCU and Bluetooth Low-Energy (BLE) communication, and a Raspberry Pi3 as a proxy for the smartphone. With our Collaborative Heart Rate Inference System (CHRIS), we obtain a set of Pareto-optimal configurations demonstrating the same MAE as State-of-Art (SoA) algorithms while consuming less energy. For instance, we can achieve approximately the same MAE of TimePPG-Small [1] (5.54 BPM MAE vs. 5.60 BPM MAE) while reducing the energy by 2.03×, with a configuration that offloads 80% of the predictions to the phone. Furthermore, accepting a performance degradation to 7.16 BPM of MAE, we can achieve an energy consumption of 179 uJ per prediction, 3.03× less than running TimePPG-Small on the smartwatch, and 1.82× less than streaming all the input data to the phone.

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.

Ultralow-resolution infrared (IR) array sensors offer a low cost, energy efficient, and privacy-preserving solution for people counting, with applications, such as occupancy monitoring and visitor flow analysis in private and public spaces. Previous work has shown that deep learning (DL) can yield superior performance on this task. However, the literature was missing an extensive comparative analysis of various efficient DL architectures for IR array-based people counting, that considers not only their accuracy but also the cost of deploying them on memory- and energy-constrained Internet of Things (IoT) edge nodes. Such analysis is key for system designers, since it helps them select the most appropriate DL model given the constraints of their target hardware. In this work, we address this need by comparing six different DL architectures on a novel data set composed of IR images collected from a commercial$8\times8$array, which we made openly available. With a wide architectural exploration of each model type, we obtain a rich set of Pareto-optimal solutions, spanning cross-validated balanced accuracy scores in the 55.70%–82.70% range. When deployed on a commercial microcontroller (MCU) by STMicroelectronics, the STM32L4A6ZG, these models occupy 0.41–9.28kB of memory, and require 1.10–7.74 ms per inference, while consuming 17.18–$120.43 \mu \text{J}$of energy. Our models are significantly more accurate than a previous deterministic method (up to +39.9%), while being up to$3.53\times $faster and more energy efficient. So, our work serves also as a demonstration that DL can not only achieve higher accuracy but also higher efficiency compared to classic algorithms for this type of task. Further, our models’ accuracy is comparable to state-of-the-art DL solutions on similar resolution sensors, despite a much lower complexity. All our models enable continuous, real-time inference on an MCU-based IoT node, with years of autonomous operation without battery recharging.

Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection. However, many other relevant applications of DL, especially at the edge, are based on time-series processing and require models with unique features, for which NAS is less explored. This work focuses in particular on Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged as a promising alternative to more complex recurrent architectures. We propose the first NAS tool that explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive-field and number of features in each layer. The proposed approach searches for networks that offer good trade-offs between accuracy and number of parameters/operations, enabling an efficient deployment on embedded platforms. Moreover, its fundamental feature is that of being lightweight in terms of search complexity, making it usable even with limited hardware resources. We test the proposed NAS on four real-world, edge-relevant tasks, involving audio and bio-signals: (i) PPG-based Heart-Rate Monitoring, (ii) ECG-based Arrythmia Detection, (iii) sEMG-based Hand-Gesture Recognition, and (iv) Keyword Spotting.Results show that, starting from a single seed network, our method is capable of obtaining a rich collection of Pareto optimal architectures, among which we obtain models with the same accuracy as the seed, and 15.9-152× fewer parameters. Moreover, the NAS finds solutions that Pareto-dominate state-of-the-arthand-tuned models for 3 out of the 4 benchmarks, and are Pareto-optimal on the fourth (sEMG). Compared to three state-of-the-art NAS tools, ProxylessNAS, MorphNet and FBNetV2, our method explores a larger search space for TCNs (up to 1012×) and obtains superior solutions, while requiring low GPU memory and search time. We deploy our NAS outputs on two distinct edge devices, the multicore GreenWaves Technology GAP8 IoT processor and the single-core STMicroelectronics STM32H7 microcontroller. With respect to the state-of-the-art hand-tuned models, we reduce latency and energy of up to 5.5× and 3.8× on the two targets respectively, without any accuracy loss.

Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the sequence-to-sequence mapping problem at the heart of Neural Machine Translation (NMT). In this work, we address the specific issues of collaborative NMT, such as estimating the latency required to generate the (unknown) output sequence, and show how existing CI methods can be adapted to these applications. Our experiments show that CI can reduce the latency of NMT by up to 44% compared to a non-collaborative approach.

Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node.With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RGMCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57$\mu$J.

Establishing reliable correspondences from a putative correspondence set is a challenging task. Most of state-of-the-art methods utilize the local context and global context to address the task. However, the local and global context often contains large number of outliers, which have a negative impact on capturing scene geometry. In this paper, we propose a Multi-scale Attention Network (called MANet), which introduces the attention mechanism for feature matching, to improve the ability of capturing scene geometry. Specifically, we first fuse the features of low and high levels by an multi-scale strategy network to enhance the representative ability of features. Then, we propose an attentive PointCN block and an attentive pooling layer, to discriminatively capture global context and local context information, respectively. We demonstrate through extensive experiments on both indoor and outdoor datasets that MANet provides a significant improvement in the performance of the two-view geometry and correspondences accuracy compared to the state-of-the-art methods.

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.

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.

Finding the energy-optimal route in the context of parcel delivery with electric vehicles (EVs) is more complicated than for conventional internal combustion engine (ICE) vehicles, where the energy cost of a path is mostly determined by the total traveled distance. In the case of EV delivery, the total energy consumption strongly depends on the order of delivery because the efficiency of the EV is affected by how the transported weight changes over time as it directly affects the battery efficiency. This makes impossible to find an optimal solution using traditional routing algorithms such as the traveling salesman problem (TSP) using a static quantity (e.g., distance) as a metric.In this paper, we propose a solution for the least-energy delivery problem using EVs; we implement an electric truck simulator and evaluate different static metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties, and is sensibly faster than state-of-the-art TSP heuristic algorithms.

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.

Modern low power designs use multiple knobs for concurrent dynamic and leakage power optimization; supply voltage and threshold voltage are the most adopted. An efficient control of these knobs needs management policies aware of the power breakdown. This implies the availability of smart on-chip strategies for dynamic and leakage power estimation at runtime. In this paper, we address this issue proposing the implementation of embedded dynamic/static power meters that use an optimized regression model fed with data collected from in-situ activity monitors. The number of sensors, their bitwidth and optimal placement are obtained through an automated design flow. The methodology works for general logic and applies not just to processor cores, but also to application-specific designs. We apply our solution to a representative class of benchmarks, showing that it can achieve an average estimation error smaller than 3%, with limited area and power overheads.

Lifetime maximization is a key challenge in battery-powered multi-sensor devices. Battery-aware power management strategies combine task scheduling with dynamic voltage scaling (DVS), accounting for the fact that the power drawn by the device is different from that provided by the battery due to its many non-idealities. However, state-of-the-art techniques in this field do not take into account several important aspects, such as the impact of sensing tasks on the overall power demand, the (operating point dependent) losses due to multiple DC-DC conversions, and the dynamic modifications in battery efficiency caused by different distributions of the currents in the temporal and in the frequency domains. In this work, we propose a novel approach to identify optimal power management solutions, that addresses all these limitations. Specifically, using advanced battery and DC-DC converter models, we propose methods to explore the scheduling space both statically (at design time) and dynamically (at runtime), accounting not only for computation tasks, but also for communication and sensing. With this method, we show that the battery lifetime can be increased by as much as 23.36% if an optimal power management strategy is adopted.

The modeling of electrical energy storage systems has received a lot of attention during the last two decades, as a consequence of the remarkable increase of battery-powered devices. However, automated extraction of a cell/pack characteristics from available product data, has been proposed only more recently. Although the automated modeling of a battery discharge performance is already well reported in the literature, extraction of the basic features of the charging phase is less common since most commercial chargers work at the standard constant current-constant voltage (CC-CV) protocol at fixed working conditions. Nevertheless, many rechargeable batteries allow different charging current rates. Therefore, for a full modeling and analysis of the battery behavior during the charging phase, the basic features, like the internal resistance and the open circuit voltage (Rch and VOCch), should be modeled. Unfortunately, only a few data regarding the charging phase are available from time-based plots in datasheets. This work presents a method for modeling the fundamental characteristics of the charging phase of a battery, starting from typical multi-plot time-based charts.

Drones are becoming increasingly popular in the commercial market for various package delivery services. In this scenario, the mostly adopted drones are quad-rotors (i.e., quadcopters). The energy consumed by a drone may become an issue, since it may affect (i) the delivery deadline (quality of service), (ii) the number of packages that can be delivered (throughput) and (iii) the battery lifetime (number of recharging cycles). It is thus fundamental try to find the proper compromise between the energy used to complete the delivery and the speed at which the quadcopter flies to reach the destination. In order to achieve this, we have to consider that the energy required by the drone for completing a given delivery task does not exactly correspond to the energy requested to the battery, since the latter is a non-ideal power supply that is able to deliver power with different efficiencies depending on its state of charge. In this paper, we demonstrate that the proposed battery-aware delivery scheduling algorithm carries more packages than the traditional delivery model with the same battery capacity. Moreover, the battery-aware delivery model is 17% more accurate than the traditional delivery model for the same delivery scheme, which prevents the unexpected drone landing.

Circuit-equivalent battery models are considered defacto standard for modeling and simulation of digital systems due to many practical advantages. In spite of the many variants of models proposed in the literature, none of them accounts for one important feature of the battery dynamics, namely, the dependency on the frequency of current load profile. For a given average current value, current loads with different spectral distributions may have quite different impacts on the battery discharge. This is a very well-know issue in the design of hybrid energy storage systems, where different types of storages devices are used, each with different storage efficiency for different load frequency ranges. We propose a basic modification to a state-of-the-art model that incorporates this load frequency dependency, as well as a methodology to identify the frequency-sensitive parameters of the model from publicly available data (e.g., datasheets). The results show that frequency-agnostic models can significantly overestimate the battery state-of-charge, and that this effect is far from being negligible.

Despite the wide body of literature on the sizing of energy storage devices available in the domain of electrical energy systems, the problem has not drawn much attention in the area of battery-powered electronic systems. It is well-known that the straightforward method of sizing battery as the product of an expected duration and the average load current always underestimates the actual capacity that the battery can supply. The variability of the workload and of its spectral distribution will in fact affect the effective capacity of battery that cannot be ignored. This paper proposed a methodology to compute the required capacity of a battery based on the properties of the workload; in particular it accounts for both the impact of the distribution of the current load and of its frequencies, and determines corrective factors for both effects to be used for the calculation of the actual capacity. We used a frequency-sensitive circuit-equivalent battery model to validates our method on three synthetic and two real workloads. Simulation results show that even for workload with same average current, the required capacity can be as much as 70% larger than the capacity estimated using a traditional method.

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.

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.

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging.

The Ragone chart is a pictorial representation to express the well-known the trade-off between available energy vs. power of different classes of energy storage devices (ESDs) like batteries or supercapacitors. Ragone charts, however, do not normally provide information about individual devices, which is an essential requirement for the actual design of the energy storage sub-system.

In deeply scaled CMOS technologies, device aging causes cores performance parameters to degrade over time. While accurate models to efficiently assess these degradation exist for devices and circuits, no reliable model for processor cores has gained strong acceptance in the literature. In this work, we propose a methodology for deriving an NBTI aging model for embedded cores. Based on an accurate characterization on the netlist of the core, we were able to (1) prove the independence of the aging on the workload (i.e., executed instructions), and (2) calculate an equivalent average constant aging factor that justifies the use of the baseline model template. We derived and assessed the proposed model by using a RISC-like processor core implemented in a 45nm process technology as a reference architecture, achieving a maximum error of 2.2% against simulated data on the core netlist.