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With the aim of obtaining time/space improvements in classic Data Structures,an emerging trend is to combine Machine Learning techniques with the onesproper of Data Structures. This new area goes under the name of Learned DataStructures. The motivation for its study is a perceived change of paradigm inComputer Architectures that would favour the use of Graphics Processing Unitsand Tensor Processing Units over conventional Central Processing Units. Inturn, that would favour the use of Neural Networks as building blocks ofClassic Data Structures. Indeed, Learned Bloom Filters, which are one of themain pillars of Learned Data Structures, make extensive use of Neural Networksto improve the performance of classic Filters. However, no use of NeuralNetworks is reported in the realm of Learned Indexes, which is another mainpillar of that new area. In this contribution, we provide the first, and muchneeded, comparative experimental analysis regarding the use of Neural Networksas building blocks of Learned Indexes. The results reported here highlight theneed for the design of very specialized Neural Networks tailored to LearnedIndexes and it establishes a solid ground for those developments. Our findings,methodologically important, are of interest to both Scientists and Engineersworking in Neural Networks Design and Implementation, in view also of theimportance of the application areas involved, e.g., Computer Networks and DataBases.

On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes