FPGA-Acceleration of machine learning in cloud computing, a case study using logistic regression - Κορομηλάς, Ηλίας (Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων VLSI, 2017)
Machine Learning (ML) tasks have revolutionized the ways in which we interact with large-scale, imperfect, real-world data and are becoming pervasive in a broad range of applications, and in a broad range of systems (from embedded systems to data centers). However, it turns out that traditional compute systems are not well-suited to the incredibly high computational requirements of modern ML approaches. As computer architectures evolve toward heterogeneous multi-cores composed of a mix of cores and hardware accelerators, designing hardware accelerators for ML techniques can simultaneously achieve high efficiency and broad application scope.