Modular approach to data preprocessing in ALOHA and application to a smart industry use case

TL;DR

This paper presents a modular, plugin-based data preprocessing approach integrated into the ALOHA tool flow, enabling flexible customization for deep learning applications in smart industry scenarios on low-power embedded platforms.

Contribution

It introduces a modular, extensible preprocessing framework within ALOHA, facilitating easy adaptation to various datasets and use cases without advanced skills.

Findings

Effective preprocessing demonstrated on a keyword spotting use case

Framework supports easy extension to new use cases

Reduces effort and costs in deploying deep learning on embedded systems

Abstract

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.