Technology scaling and increasing System-on-Chip (SoC) complexity exacerbate reliability challenges arising from both structural defects and runtime-dependent failures, including Silent Data Corruptions (SDCs) that evade traditional error detection mechanisms. Structural testing remains essential for detecting modeled faults such as stuck-at faults; however, it is inherently limited in capturing failures that arise under dynamic operating conditions. In contrast, functional testing can expose workload-dependent failures, albeit at the cost of high testing overhead and largely unguided workload generation. This paper presents an agentic testing framework that integrates Large Language Models (LLMs) with Reinforcement Learning (RL) and Tree-structured Parzen Estimators (TPE) to guide functional workload generation and Automatic Test Pattern Generation (ATPG) settings under user-defined constraints. The proposed approach leverages feedback-driven optimization to steer test generation toward failure-prone behaviors while reducing reliance on manual expertise. Experimental evaluation on a RISC-V processor core demonstrates that the method outperforms manually generated workloads for functional testing, while experiments on six benchmark circuits show test quality comparable to expert-generated ATPG scripts for structural testing, with improved efficiency and scalability.

Federated Learning (FL) is a transformative paradigm in machine learning that enables collaborative model training across decentralized devices, ensuring data remains securely stored locally to enhance privacy. Traditional FL techniques, such as FedAvg, rely on gradient aggregation to construct a global model. However, these approaches often incur significant communication overhead and pose privacy risks, as gradient updates can inadvertently expose sensitive information. To address these challenges, this paper presents a novel FL framework that formulates federated optimization as a Multi-Objective Optimization (MOO) problem and proposes Sync-GWO, an innovative adaptation of the Grey Wolf Optimizer (GWO) tailored specifically for FL. In contrast to conventional population-based methods that require full population transfers, Sync-GWO leverages synchronized Pseudo-Random Number Generators (PRNGs) to eliminate population data transmission. The proposed approach significantly reduces communication overhead while enhancing privacy by avoiding gradient exchange. Experimental results on COVID-19 pandemic-related Internet of Medical Things (IoMT) data demonstrate that Sync-GWO achieves up to 15% higher accuracy and $2.5\times$ improved F1-scores compared to FedAvg, while reducing communication costs by over 99% to approximately 1 KB per round. Sync-GWO is particularly well-suited for scenarios requiring high privacy and extreme communication efficiency, such as those encountered in IoMT. Furthermore, Sync-GWO exhibits robust performance on imbalanced datasets and in optimization settings involving non-differentiable objectives, where gradient-based methods are often less effective.

Accurate Blood Glucose (BG) prediction is essential for enabling glycemic control in individuals with Type 1 Diabetes Mellitus (T1DM), particularly within Smart and Connected Health (SCH) systems that integrate Continuous Glucose Monitoring (CGM) and automated insulin delivery. The adaptability of Large Language Models (LLMs) provides a promising foundation for unified, fine-tunable forecasting models. We introduce DiabLLM, a framework based on two recent LLM-based architectures: Time-LLM, which incorporates a lightweight projection layer and alignment techniques to transform time-series data into embeddings interpretable by pre-trained LLMs, and Chronos, which employs time-series-aware tokenization and quantization to convert continuous inputs into discrete sequences for forecasting. Both models process 30-minute sequences of six historical BG values and predict 30- and 45-minute horizons. Experimental results on the OhioT1DM and D1NAMO datasets demonstrate that DiabLLM outperforms state-of-the-art baselines, including a Deep Reinforcement Learning model and an ensemble of LSTM, GRU, and WaveNet, achieving up to 27% improvement in RMSE and 37% in MAE. To enhance robustness to noisy and missing input data, a denoising autoencoder was employed for input reconstruction, yielding improved predictive performance. In addition, knowledge distillation was shown to significantly compress the model, making it a practical candidate for efficient deployment on resource-constrained edge devices without compromising accuracy.

Flexible Electronics (FE) have emerged as a promising platform for extreme edge applications that demand attributes tailored to the application domain, such as ultra-low cost, low power consumption, mechanical flexibility, biocompatibility, and environmental sustainability. While advances in printed and flexible device technologies have demonstrated the feasibility of sensing, computing, and communication on deformable substrates, the design and implementation of FE-based systems remain limited by traditional Electronic Design Automation (EDA) workflows, which are complex, time-intensive, and largely inaccessible to non-experts. In parallel, recent progress in Large Language Models (LLMs) has enabled automation across multiple stages of integrated circuit design; however, existing approaches exclusively target conventional silicon technologies and are not designed to address the unique constraints of FE. This work introduces the first LLM-driven framework for end-to-end hardware design automation in flexible electronics. The proposed methodology supports Register-Transfer Level (RTL) generation, logic synthesis, and cross-layer Power–Performance–Area (PPA) Design Space Exploration (DSE) for bespoke Machine Learning (ML) classifiers. Experimental results demonstrate the feasibility and effectiveness of the approach in generating resource-efficient hardware designs optimized for FE, thereby lowering barriers to adoption and accelerating the development of personalized, application-specific FEs.

The increasing complexity of semiconductor design, along with stringent performance, power, and time-to-market requirements, has outpaced the capabilities of traditional Electronic Design Automation (EDA) methodologies. Conventional design workflows rely on manual intervention for critical tasks such as hardware description, synthesis optimization, and verification, leading to inefficiencies and scalability limitations. Large Language Models (LLMs) present a transformative approach by automating key stages of the design pipeline, enabling intelligent synthesis tuning, test generation, and security analysis. This paper introduces ChipMind, an LLM-driven framework comprising specialized agents and modules for digital and analog chip design. ChipMind integrates AI-driven methodologies to enhance design efficiency, accelerate prototyping, and optimize key design trade-offs, thereby addressing fundamental challenges in modern semiconductor development.

The ever-growing Internet of Things (IoT) connections drive a new type of organization, the intelligent enterprise. In intelligent enterprises, machine learning-based models are adopted to extract insights from data. Due to these traditional models’ efficiency and privacy challenges, a new federated learning (FL) paradigm has emerged. In FL, multiple enterprises can jointly train a model to update a final model. However, first, FL-trained models usually perform worse than centralized models, especially when enterprises’ training data are nonindependent and identically distributed (IID). Second, due to the centrality of FL and the untrustworthiness of local enterprises, traditional FL solutions are vulnerable to poisoning and inference attacks and violate privacy. Third, the continuous transfer of parameters between enterprises and servers increases communication costs. Therefore, to this end, the FedAnil+ model is proposed, a novel, lightweight, and secure Federated Deep Learning Model that includes three main phases. In the first phase, the goal is to solve the data type distribution skew challenge. Addressing privacy concerns against poisoning and inference attacks is given in the second phase. Finally, to alleviate the communication overhead, a novel compression approach is proposed that significantly reduces the size of the updates. The experiment results validate that FedAnil+ is secure against inference and poisoning attacks with better accuracy. In addition, in terms of model accuracy (13%, 16%, and 26%), communication cost (17%, 21%, and 25%), and computation cost (7%, 9%, and 11%) improvements over existing approaches. The FedAnil+ code is available on GitHub.

Most speech recognition systems utilize cloud computing for model training and updates. Speech data, being personally identifiable information (PII), encompasses personal, privacy-sensitive, and regulated content. Relying on centralized servers or third parties can threaten confidential data, resulting in privacy breaches. Therefore, privacy issues and strict regulations (e.g., EU’s general data protection regulation, California’s CCPA, and the Privacy Act in Australia) limit the availability of large data sets. The scarcity of data sets is particularly pronounced in less-represented languages, like Persian, adversely impacting innovation and data-driven product development. To overcome the challenges posed by the scarcity of data sets and privacy concerns, for the first time, we propose a novel federated learning (FL) solution for Persian Spoken Isolated Digit Recognition. This proposed technique bridges the gap between privacy and utility by enabling the training of an algorithm using decentralized data sets stored on edge devices or servers, without the need for data exchange. Nonindependent and identically distributed data (non-IID), such as unique speaker accents, poses a challenge in speech recognition, especially in an FL setup. Regrettably, this challenge has largely been overlooked in existing techniques and methodologies. To address this, we present an innovative personalized clustered FL (PCFL) approach that successfully exploits similarities among the private data distributions and captures distinctive characteristics inherent in each client’s data in order to train models. The experimental results show that while the proposed solution significantly addresses privacy concerns, it has a negligible performance loss compared to centralized model training techniques.

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

Serverless computing has revolutionized the world of cloud-based and event-driven applications with the introduction of Function as a Service (FaaS) as the latest cloud computing model. This computational model increases the level of abstraction from the infrastructure and breaks the program into small units called functions. Thus, it brings benefits, such as ease of development, saving resources, and reducing product launch time for enterprises and developers. Thanks to the scale-to-zero feature of this computational model, idle functions with no traffic will be depreciated from memory. However, this cost-saving approach adversely impacts delay leading to the cold start problem. Unfortunately, the existing solutions to alleviate the cold start delay are not resource efficient as they follow a fixed policy over time. Thereby, this article proposes a novel two-layer adaptive approach to tackle this issue. The first layer utilizes a holistic reinforcement learning algorithm to discover the function invocation patterns over time for determining the best time to keep the containers warm. The second layer is designed based on a long short-term memory (LSTM) to predict the function invocation times in the future to determine the required prewarmed containers. The experimental results on the Openwhisk platform show that the proposed approach reduces the memory consumption by 12.73% and improves the execution invocations on prewarmed containers by 22.65% compared to the Openwhisk platform.

As the trajectory of the Internet of Things (IoT) moving at a rapid pace and with the rapid worldwide development and public embracement of wearable sensors, these days, most companies and organizations are awash in massive amounts of data. Determining how to profit from data deluge can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business. The market has already seen a level of monetization across vertical domains in the form of layering connected devices with a variety of Software-as-a-Service (SaaS) choices, such as subscription plans or smart device insights. Out of this arena is evolving a “machine economy” in which the ability to correctly monetize data rather than simply hoard it, will provide a significant advantage in a competitive digital environment. The recent advent of the technological advances in the fields of big data, analytics, and artificial intelligence (AI) has opened new avenues of competition, where data are utilized strategically and treated as a continuously changing asset able to unleash new revenue opportunities for monetization. Such growth has made room for an onslaught of new tools, architectures, business models, platforms, and marketplaces that enable organizations to successfully monetize data. In fact, emerging business models are striving to alter the power balance between users and companies that harvest information. Start-ups and organizations are offering to sell user data to data analytics companies and other businesses. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by: 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets. This article will address all important aspects of IoT data monetization with more focus on the healthcare industry and discuss the corresponding challenges, such as data management, scalability, regulations, interoperability, security, and privacy. In addition, it presents a holistic reference architecture for the healthcare data economy with an in-depth case study on the detection and prediction of cardiac anomalies using multiparty computation (MPC) and privacy-preserving machine learning (PPML) techniques.

As The trajectory of the Internet of Things (IoT) is moving at a rapid pace, most companies and organizations are awash and drowning in massive amounts of data. Determining how to profit from data deluge and unlock its value can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business [A1]. The market has already seen a level of monetization across vertical domains e.g., in the form of layering connected devices with a variety of Insights-as-a- Service options. Out of this arena, the data economy concept has been evolving, characterized by correctly monetizing data rather than simply hoarding it, which will provide a significant advantage in a competitive digital environment [A1]. The recent advent of technological advances in the fields of Big Data, Analytics, and Artificial Intelligence (AI) has opened new avenues of competition, where IoT data is considered a living and evolving entity that can unlock enormous opportunities for monetization. Such growth brought forth a slew of new tools, architectures, business models, platforms, and marketplaces, enabling organizations to monetize data successfully. In this context, emerging business models also strive to alter the power balance between users and companies that harvest information by utilizing usage policy enforcement and privacypreserving machine learning techniques. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets [A1].

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth.

The impending Internet of Things (IoT) wave is promising to affect every aspect of our daily lives, ranging from smart things to smart buildings, smart cities, and smart environments. A lot of attention has been devoted to the tsunami of data produced by IoT, and the related means of extracting useful actionable information from it, spawning efforts in Big Data processing and machine learning. Yet, all of this does little to address the need for IoT to capture, interpret, and act on this wall of (noisy) information at the right time, at the right place, and in the right form. Conventional computing systems are a poor match to the needs of this emerging massively distributed real-time system. Hence, alternative computing techniques present an attractive alternative, trading off computational resolution for significant gains in quality-of-service energy efficiency and robustness. This observation is based on the conjecture that most applications related to IoT have an inherent error resilience and are evolutionary (that is, learning-based). Alternative computing strategies may be conceived at every level of the design hierarchy, starting from the device level with novel 3-D nonvolatile memory/logic combinations, or at the architectural level by shifting away from the traditional von Neumann architecture to different computing paradigms such as neuromorphic and/or stochastic computation all the way up to the algorithmic and data representation levels.

Bias Temperature Instability (BTI) and process variation introduce remarkable unpredictability to Custom Instructions (CIs) manufactured at nano-scale technology. Moreover, shrinking the feature size to nanometer levels makes soft error another critical issue of CIs. To tackle these factors, we propose a reliability-aware cross-layer CI screening method. By adding an intermediate phase between the CI generation and CI selection phases, this method enables designers to prune the outputs of the generation phase in order to guarantee that synthesized CIs meet the required reliability constraints. For this purpose, a holistic framework is developed to analyze the combined effects of the BTI and process variation as well as the soft error on the CIs by making a link between circuit-level and system-level information. Based on this information collected from different layers of abstraction, the screening method prunes those CIs which cannot meet the reliability constraints. Experiments illustrate that BTI-unaware CI selection techniques may not meet the desired lifetime because of BTI-induced delay shift of CIs. Moreover, according to the results, a remarkable percentage of CIs is vulnerable to soft error and should not be fed into CI selection phase.