In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep learning life cycle processes, such as model training, model evaluation, model deployment, model inference, ground truth collecting, data pre-processing, and training data management. To the best of our knowledge, such a whole deep learning development environment has not been built before. MiniDeep uses Amazon Web Services (AWS) as the backend platform of a deep learning tuning management model. In the edge device, the OpenVino enables deep learning inference acceleration at the edge. To perform a deep learning life cycle job, MiniDeep proposes a mini deep life cycle (MDLC) system which is composed of several microservices from the cloud to the edge. MiniDeep provides Train Job Creator (TJC) for training dataset management and the models’ training schedule and Model Packager (MP) for model package management. All of them are based on several AWS cloud services. On the edge device, MiniDeep provides Inference Handler (IH) to handle deep learning inference by hosting RESTful API (Application Programming Interface) requests/responses from the end device. Data Provider (DP) is responsible for ground truth collection and dataset synchronization for the cloud. With the deep learning ability, this paper uses the MiniDeep platform to implement a recommendation system for AI-QSR (Quick Service Restaurant) KIOSK (interactive kiosk) application. AI-QSR uses the MiniDeep platform to train an LSTM (Long Short-Term Memory)-based recommendation system. The LSTM-based recommendation system converts KIOSK UI (User Interface) flow to the flow sequence and performs sequential recommendations with food suggestions. At the end of this paper, the efficiency of the proposed MiniDeep is verified through real experiments. The experiment results have demonstrated that the proposed LSTM-based scheme performs better than the rule-based scheme in terms of purchase hit accuracy, categorical cross-entropy, precision, recall, and F1 score.

中文翻译：

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MiniDeep：具有基于深度学习的 MINI-PC 和 AI-QSR 系统的独立 AI-Edge 平台

在本文中，我们提出了一个新的 AI（人工智能）边缘平台，称为“MiniDeep”，它提供了一个基于云边缘架构的独立深度学习平台。这个AI-Edge平台为开发者提供了一个完整的深度学习开发环境来设置他们的深度学习生命周期流程，例如模型训练、模型评估、模型部署、模型推理、ground truth收集、数据预处理和训练数据管理。据我们所知，之前还没有建立过这样一个完整的深度学习开发环境。MiniDeep 使用 Amazon Web Services (AWS) 作为深度学习调优管理模型的后端平台。在边缘设备中，OpenVino 可在边缘实现深度学习推理加速。要执行深度学习生命周期工作，MiniDeep 提出了一个迷你深度生命周期（MDLC）系统，它由从云端到边缘的多个微服务组成。MiniDeep 提供 Train Job Creator (TJC) 用于训练数据集管理和模型的训练计划，以及 Model Packager (MP) 用于模型包管理。它们都基于多个 AWS 云服务。在边缘设备上，MiniDeep 提供推理处理程序 (IH)，通过托管来自终端设备的 RESTful API（应用程序编程接口）请求/响应来处理深度学习推理。数据提供者 (DP) 负责云的地面实况收集和数据集同步。借助深度学习能力，本文利用MiniDeep平台实现了AI-QSR（Quick Service Restaurant）KIOSK（interactive kiosk）应用的推荐系统。AI-QSR 使用 MiniDeep 平台来训练基于 LSTM（长短期记忆）的推荐系统。基于 LSTM 的推荐系统将 KIOSK UI（用户界面）流转换为流序列，并通过食物建议执行顺序推荐。在本文的最后，通过实际实验验证了所提出的 MiniDeep 的效率。实验结果表明，所提出的基于 LSTM 的方案在购买命中率、分类交叉熵、精度、召回率和 F1 分数方面优于基于规则的方案。通过实际实验验证了所提出的 MiniDeep 的效率。实验结果表明，所提出的基于 LSTM 的方案在购买命中率、分类交叉熵、精度、召回率和 F1 分数方面优于基于规则的方案。通过实际实验验证了所提出的 MiniDeep 的效率。实验结果表明，所提出的基于 LSTM 的方案在购买命中率、分类交叉熵、精度、召回率和 F1 分数方面优于基于规则的方案。