Introduction to Edgematic

Edgematic is a cutting-edge UI-based development platform design for Edge AI applications. It empowers developers to streamline the creation and deployment of Edge AI applications with minimal coding requirements. Built on Palette, Edgematic provides a low/no-code environment to construct and evaluate optimized pipelines. It uses GStreamer alongside SiMa’s engine for model quantization, compilation, and deployment, making it an essential tool for accelerating Edge AI workflows.

This document is a comprehensive resource for understanding, exploring, and maximizing the potential of Edgematic, a cutting-edge UI-based development platform designed for Edge AI applications. Whether you are a seasoned developer or new to Edge AI, this guide will help you navigate through the platform’s capabilities, ensuring a seamless experience as you build, test, and deploy advanced machine learning applications.

Typical users include:

  • AI developers looking to optimize their models for edge deployment.

  • Data scientists seeking a user-friendly platform for model evaluation and quantization.

  • System integrators aiming to create scalable, efficient AI solutions.

  • Anyone interested in exploring the intersection of AI, edge devices, and low-latency applications.

Key Features

Edgematic offers a range of features to simplify your Edge AI development process:

  • Pre-Created Pipelines: Test and evaluate pipelines built for real-world use cases.

  • Model Import and Evaluation: Bring your own models to obtain performance metrics like FPS and FPS/W.

  • Seamless Integration: Drag and drop compiled models from Palette into Edgematic for automatic pre/post-processing optimization.

  • Future Enhancements: Look forward to features like application development, local deployment, online model quantization and compilation, and MLSoC device management.

Benefits

Edgematic saves you time and effort by automating complex processes, providing a streamlined environment for deploying highly optimized Edge AI applications. By leveraging its capabilities, you can focus on innovation and efficiency without worrying about the intricacies of edge hardware configurations or low-level software development.

Getting Started

In the sections ahead, you will find step-by-step instructions, examples, and best practices to help you get started with Edgematic. Whether you want to evaluate existing pipelines, deploy a custom model, or build a comprehensive application, this guide will provide the insights you need to succeed.

Let us begin your journey to transforming Edge AI development with Edgematic!

Layout

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The Project Explorer displays the file structure of the project. It organizes files, folders, and dependencies for easy navigation.

Key elements include:

  • Project Files: Contains project definitions, dependencies, and configuration files.

  • Plugins: Houses modules such as capsfilter, h264parse, and nmsyolov5 that can be used in your pipeline.

  • Resources: Stores additional assets and application.json.

Use the search bar to quickly locate files within the project structure.