The Spline.ai Low-Code Smart Healthcare Platform presented here is a demonstration using pneumonia and COVID-19 deep learning applications. The model is compiled and optimized using the Vitis™ AI software platform to run inference on the Kria™ KV260 starter kit with the Ubuntu 22.04 operating system. This low-code framework is designed to develop applications either as standalone or with a large fleet of Kria K26 SOM-based edge appliances in an AWS IoT Greengrass v2 platform.
Video Provided by Spline.ai
The Spline.ai Smart Healthcare Platform is developed using the Amazon IoT Greengrass v2 platform and the Kria KV260 starter kit as the edge device. A fleet of Kria K26 SOM-based edge appliances can be connected to develop low-latency, low-power, and low-cost IoT applications.
Spline.ai provides a highly scalable development platform that is extremely cost effective and suitable for multiple connected hospitals, ambulances, or hospitals on wheels use cases.
This healthcare platform is designed to enable healthcare professionals to develop a radiology flow that helps improve healthcare diagnostics, monitoring, and tracking applications.
The platform can also be used to develop scalable applications beyond healthcare, such as agricultural, robotics, pollution monitoring, and other industrial applications.
Utilizing Vitis AI Model Zoo, the smart platform can plug in a variety of high-quality deep learning models for rapid development of diverse IoT applications.
No, the app does not require any experience in FPGA design.
Customers have the option of procuring a 30-day free evaluation floating license or a node-lock permanent paid license.
Off-the-shelf COVID prediction is available for North America, Europe, and India. Customization options for other countries are available on request.
Yes, provisioning of an edge device fleet is available to manage large deployments of devices across multiple locations.
Processing of image data is secured at the edge device. The security and safety standards supported by the Kria SOM and AWS are leveraged to protect private information and patient data.
The platform can use fine-tuned and re-trained models from Vitis AI Model Zoo or other similar types of models when developing scalable IoT applications.
了解有關自適應 SOM 的所有信息,以實例解釋了自適應 SOM 為什麼適合下一代邊緣應用,以及如何在下一代邊緣應用中部署自適應 SOM。本書還強調了智能視覺提供商如何從隻有自適應 SOM 才能實現的性能、靈活性及快速開發等優勢中獲益。
對機器人的需求正在迅速增長。構建一款既安全又能與人一起工作的機器人已經夠難了。還要讓這些技術協同工作,則更具挑戰性。更複雜的是,機器學習和人工智能的加入,增加了滿足計算需求的難度。機器人專家正在轉而采用自適應計算平台,其可在一個適應未來、可擴展的自適應集成型平台上實現內建的安全保障,從而可提供低時延、確定性的多軸控製。參閱 eBook,了解更多信息。