  With the aim of obtaining time/space improvements in classic Data Structures,
an emerging trend is to combine Machine Learning techniques with the ones
proper of Data Structures. This new area goes under the name of Learned Data
Structures. The motivation for its study is a perceived change of paradigm in
Computer Architectures that would favour the use of Graphics Processing Units
and Tensor Processing Units over conventional Central Processing Units. In
turn, that would favour the use of Neural Networks as building blocks of
Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the
main pillars of Learned Data Structures, make extensive use of Neural Networks
to improve the performance of classic Filters. However, no use of Neural
Networks is reported in the realm of Learned Indexes, which is another main
pillar of that new area. In this contribution, we provide the first, and much
needed, comparative experimental analysis regarding the use of Neural Networks
as building blocks of Learned Indexes. The results reported here highlight the
need for the design of very specialized Neural Networks tailored to Learned
Indexes and it establishes a solid ground for those developments. Our findings,
methodologically important, are of interest to both Scientists and Engineers
working in Neural Networks Design and Implementation, in view also of the
importance of the application areas involved, e.g., Computer Networks and Data
Bases.
