ALOHA中数据预处理的模块化方法及其在智能行业用例中的应用
Modular approach to data preprocessing in ALOHA and application to a smart industry use case
摘要：
智能工业领域中的应用，例如使用语音命令或机器视觉系统与协作机器人交互，通常需要在异构低功耗计算平台上部署深度学习算法。自动化不同设计步骤的软件工具和框架的可用性可以支持嵌入式系统上DL算法的有效实现，从而减少相关的工作量和成本。接受框架的一个非常重要的方面是其可扩展性，即能够容纳不同的数据集并定义定制的预处理，而不需要高级技能。本文提出了一种模块化的方法，集成到ALOHA工具流，以支持数据预处理和转换管道。这是通过可定制的插件实现的，并允许轻松扩展工具流以包含新的用例。为了证明该方法的有效性，我们提出了一些实验结果相关的关键字定位用例，我们概述了可能的扩展到不同的用例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
