  Applications in the smart industry domain, such as interaction with
collaborative robots using vocal commands or machine vision systems often
requires the deployment of deep learning algorithms on heterogeneous low power
computing platforms. The availability of software tools and frameworks to
automatize different design steps can support the effective implementation of
DL algorithms on embedded systems, reducing related effort and costs. One very
important aspect for the acceptance of the framework, is its extensibility,
i.e. the capability to accommodate different datasets and define customized
preprocessing, without requiring advanced skills. The paper addresses a modular
approach, integrated into the ALOHA tool flow, to support the data
preprocessing and transformation pipeline. This is realized through
customizable plugins and allows the easy extension of the tool flow to
encompass new use cases. To demonstrate the effectiveness of the approach, we
present some experimental results related to a keyword spotting use case and we
outline possible extensions to different use cases.
