Dr. Ovidiu Vermesan holds a PhD degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are intelligent systems integration, mixed-signal embedded electronics, analogue neu-ral networks, edge artificial intelligence and cognitive communication systems. Dr. Ver-mesan received SINTEF’s 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinat-ed and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, Archi-tectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, Horizon Eu-rope and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for AI, IoT and Edge Continuum Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) project.

• 1 Advancing Edge AI Perception Platforms and Sensor Fusion for Last-Mile Delivery Autonomous Vehicles 2 AIDGE: A Framework for Deep Neural Network Development, Training and Deployment on the Edge 3 A scalable and flexible interconnect-based dataflow architecture for Edge AI Inference 4 Federated Learning for Malware Detection in Edge devices 5 Image Signal Processor (ISP) Tuning using Machine Learning (ML) methods 6 Using Edge AI in IoT devices for Smart Agriculture: AutonomousWeeding

Dr. Ovidiu Vermesan holds a PhD degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are intelligent systems integration, mixed-signal embedded electronics, analogue neu-ral networks, edge artificial intelligence and cognitive communication systems. Dr. Ver-mesan received SINTEF’s 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinat-ed and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, Archi-tectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, Horizon Eu-rope and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for AI, IoT and Edge Continuum Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) project.

• 1 Advancing Edge AI Perception Platforms and Sensor Fusion for Last-Mile Delivery Autonomous Vehicles 2 AIDGE: A Framework for Deep Neural Network Development, Training and Deployment on the Edge 3 A scalable and flexible interconnect-based dataflow architecture for Edge AI Inference 4 Federated Learning for Malware Detection in Edge devices 5 Image Signal Processor (ISP) Tuning using Machine Learning (ML) methods 6 Using Edge AI in IoT devices for Smart Agriculture: AutonomousWeeding

Dr. Ovidiu Vermesan holds a PhD degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are intelligent systems integration, mixed-signal embedded electronics, analogue neu-ral networks, edge artificial intelligence and cognitive communication systems. Dr. Ver-mesan received SINTEF’s 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinat-ed and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, Archi-tectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, Horizon Eu-rope and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for AI, IoT and Edge Continuum Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) project.

• 1 Advancing Edge AI Perception Platforms and Sensor Fusion for Last-Mile Delivery Autonomous Vehicles 2 AIDGE: A Framework for Deep Neural Network Development, Training and Deployment on the Edge 3 A scalable and flexible interconnect-based dataflow architecture for Edge AI Inference 4 Federated Learning for Malware Detection in Edge devices 5 Image Signal Processor (ISP) Tuning using Machine Learning (ML) methods 6 Using Edge AI in IoT devices for Smart Agriculture: AutonomousWeeding