Implementation of Machine Learning Algorithms on Hardware Accelerators

Nowadays, cutting-edge technology, innovation and efficiency are the cornerstones on which industries are based. Therefore, prognosis and health management have started to play a key role in the prevention of crucial faults and failures. Recognizing malfunctions in a system in advance is fundamental both in economic and safety terms. This obviously requires a lot of data – mainly information from sensors or machine control - to be processed, and it’s in this scenario that Machine Learning comes to the aid. This thesis aims to apply these methodologies to prognosis in automatic machines and has been carried out at LIAM lab (Laboratorio Industriale Automazione Macchine per il packaging), an industrial research laboratory born from the experience of leading companies in the sector. Machine learning techniques such as neural networks will be exploited to solve the problems of classification that derive from the system in exam. Such algorithms will be combined with systems identification techniques that performs an estimate of the plant parameters and a feature reduction by compressing the data. This makes easier for the neural networks to distinguish the different operating conditions and perform a good prognosis activity. Practically the algorithms will be developed in Python and then implemented on two hardware accelerators, whose performance will be evaluated.