The application of Artificial Intelligence (AI) to the design and development of high-performance electronic systems has matured significantly in recent years, moving from theoretical proposals to tangible deployments that are beginning to make a real difference in people’s lives. From systems that assist clinicians in early disease detection to embedded devices that help farmers optimize crop yield under changing climate conditions, AI-enabled electronic systems are increasingly present in domains where performance, reliability, and efficiency are not merely technical goals, they are essential requirements with direct human impact.

This special session focuses on concrete case studies that illustrate how AI-driven methodologies are being applied in practice, bridging the gap between cutting-edge research and real-world deployment. We aim to highlight the practical challenges, lessons learned, and measurable outcomes that emerge when AI-driven approaches meet the constraints of actual systems: power budgets, latency requirements, silicon area, time-to-market pressure, and operational reliability in demanding environments.

Healthcare and agriculture serve as particularly compelling examples of this convergence. In healthcare, embedded AI systems are enabling new frontiers in point-of-care diagnostics, remote patient monitoring, and medical imaging at the edge. In agriculture, smart sensing platforms and AI-powered decision systems help address critical challenges in food security, resource efficiency, and sustainable farming. Yet the scope of this session extends beyond these domains; any scenario where AI-enabled design methodologies meet real-world deployment challenges is welcome.

The topic remains inherently multidisciplinary, bringing together expertise from AI, electronic design automation (EDA), embedded systems, software engineering, and application development across sectors. This session will foster dialogue between researchers and practitioners from academia and industry, providing a platform to share reproducible insights, emerging best practices, and open challenges in the application of AI to real-world electronic systems design and deployment.

Topics of interest for this special session include, but are not limited to:

1. Case studies of AI-driven design space exploration applied to real circuits and architectures
2. Case studies of AI-enabled design methodologies and application development in real-world scenarios.
3. Real-world deployments of machine learning for power, performance, and area optimization.
4. AI-assisted hardware verification and testing in production or near-production environments.
5. Embedded AI systems for healthcare: diagnostics, monitoring, and medical imaging at the edge.
6. Smart sensing and AI-powered platforms for precision agriculture and environmental monitoring.
7. End-to-end pipelines from AI algorithm design to FPGA or ASIC deployment in real applications.
8. Case studies of AI workload optimization for edge computing and IoT devices.
9. Industry–academia collaborative experiences in AI-enabled electronic system development.
10. Benchmarking and evaluation of AI-driven methodologies in real application scenarios.

By gathering researchers and engineers around concrete experiences rather than purely theoretical contributions, this session aims to accelerate knowledge transfer and provide the DCIS community with actionable insights into the state of the art in AI-enabled electronic systems, and into the human contexts that make this work worth pursuing. 

Chairs of the special session:

Miguel Chavarrías received the Ph.D. degree from the Universidad Politécnica de Madrid (UPM), Spain, in 2017. Currently, he is Associate Professor of UPM. He has been with the Electronics and Microelectronics Design Group (GDEM) since 2011, and with the Software Technologies and Multimedia Systems for Sustainability (CITSEM) Research Center, since its creation in 2012. He has authored more than 50 journal articles and conference papers. He has participated in more than 15 research projects in competitive calls and more than 10 contracts with the industry. He was a guest researcher at Institut d’Electronique et des Technologies du numérique (IETR) laboratory of the Institut National des Sciences Apliquées (INSA) in Rennes (France) during the springs of 2012 and 2015. He received the extraordinary doctorate award granted by the academic programme and IEEE Consumer Electronics Society Chester Sall Award for the third place best paper in the IEEE Transactions on Consumer Electronics, in 2014. His research interests include edge-computing, hyperspectral imaging, computer vision and machine learning.

Gustavo M. Callico obtained an MSc in Telecommunications Engineering in 1995 and a PhD and European Doctorate in 2003, all from the University of Las Palmas de Gran Canaria (ULPGC). He joined the Institute of Applied Microelectronics (IUMA) in 1994 and, from 2000 to 2001, he was a visiting scientist at the Philips Research Laboratories (NatLab) in Eindhoven, the Netherlands, where he completed his PhD. He is currently a full professor at ULPGC and the coordinator of the Integrated Systems Design Group at IUMA. He is the co-author of eight book chapters and the author or co-author of over 250 publications (101 JCR papers) and conference contributions (152 conferences). He has participated in 27 competitive research projects (six as Principal Investigator), funded by the European Community, the Spanish government and international private industry. He also holds two international patents. Prof. Callico is an associate editor of IEEE Access since 2016 and of Microprocessors and Microsystems (Elsevier) since 2022. He is a Senior Member of the IEEE since 2019 and a member of the Euromicro Board of Directors since 2022. He was a visiting professor at the University of Pavia (Italy) in October 2015 and March 2019 and has been a member of the university’s Doctoral Council ever since. He has won eight best paper awards at various conferences and three research awards through competitive calls. Five of the ten doctoral theses he has supervised have received the award for an outstanding doctoral thesis at ULPGC. In 2023, he received an award for coordinating the research group with the highest scientific productivity at ULPGC.