Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2 modules among the following (A.A. 2024/2025 Network Science not activated)1 module among the following2° Year It will be activated in the A.Y. 2025/2026
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| Modules | Credits | TAF | SSD |
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2 modules among the following (A.A. 2024/2025 Network Science not activated)1 module among the following| Modules | Credits | TAF | SSD |
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2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Embedded AI (2024/2025)
Teaching code
4S010696
Credits
6
Coordinator
Language
English
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course aims to provide the knowledge for the creation of artificial intelligence applications on dedicated hardware platforms (FPGA, embedded processors, GPU). At the end of the course the student will have to demonstrate: knowledge of the main techniques for the design of embedded and IoT systems, starting from their specifications up to verification, synthesis and testing; knowing how to implement artificial intelligence applications on dedicated parallel architectures, with particular emphasis on GPU platforms; knowing how to connect application requirements with possible solutions by evaluating their effectiveness in terms of functional and non-functional design
constraints (e.g. correctness, performance, energy consumption); knowing how to build a project report highlighting the critical aspects resolved; knowing how to use additional paradigms for the creation of artificial intelligence applications on dedicated architectures starting from those studied in the course.
