The course starts from the basics of information representation. It then passes through logic-level networks and their design. It eventually ends to provide the basic of architectural aspects of modern computers.
The course is structured into frontal lessons and a set of directed exercitations on logic networks and IA-32 assembly programming.
Taking Dublin Indicators into account:
Knowledge and understanding
The course introduces the first concepts related to the architecture of computers. Particular emphasis is given to the understanding of the classical architectures based on the Von Neumann model. The reference text is in Italian, but standard English terminology is commonly used during the lessons as goodwill to the consultation of the international scientific literature.
Applying knowledge and understanding
The knowledge presented is always applied to the resolution of specific problems. The exercises that accompany the course are focused on solving exercises and problems, particularly at the interface between hardware and software. Often the solution methods are presented in the form of an algorithm, developing in students the ability to structure procedures that are useful in many parts of computer science, and not only in the study of the computer architecture.
The exercises, which are proposed in relation to the theoretical part presented in class, can be solved individually or in groups. The comparison with classmates, work at home or in classroom, favors the development of specific skills in students to enable the explanation of arguments to fellows and teachers. Often the exercises can be solved in many different ways and listening to the solutions proposed by other allows students to develop the ability to identify common structures, beyond the apparent superficial differences.
The numerous discussions on the different methods to solve problems allow students to improve communication skills. Specific communication of computer technology is also usually used during classes and exercises.
The study of the origins of technological solutions and their introduction motivated by quantitative considerations contributes to the students’ ability to learn in a deep way and not just superficial and repetitive. The knowledge acquired is never rigid and definitive, but it is adaptable to any evolution and change of perspective and context.