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A Unique Edge AI SDK

Ekkono provides embedded Edge AI software that makes it fast and convenient to develop and deploy smart, self-learning, and predictive features onboard your physical products.

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The SDK is designed for purpose to do streaming analytics on constrained edge devices and the tiny footprint makes it possible to run even on a sensor or a motor controller.

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Scaling up to an ECU, PLC, HMI or Edge Gateway is easy, which means that you can harmonize on Ekkono’s Edge AI for all your products.

For every project you do, it will be faster and easier since we have designed it as a toolbox that enables you to work independently with developing and evolving your smart features.

SDK

With the Ekkono SDK you can bring intelligence into your IoT devices. We currently have 4 different products in our portfolio. This is a short product overview of our SDK. Feel free to contact us for more information.

Ekkono Studio

Ekkono Studio is a workbench for developing edge machine learning solutions that can later easily be deployed on a device.

Key Features 

The Ekkono SDK is filled with things that will make it easy to develop high end machine learning solutions. Here are some of the key features of our SDK.

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Synthesis

Synthesis is a cutting-edge framework designed to advance edge machine learning and federated learning. It combines sophisticated algorithms with user-friendly experiences to streamline model management, automate anomaly detection, and simplify connectivity.

Ekkono Product Overview

We have developed an embedded software library – a Software Development Kit – built for the purpose to help developers rapidly and easily deploy edge machine learning, embedded onboard connected devices, to make them conscious, self-learning, and predictive.The main functionality focuses on streaming analytics based on sensor data in combination with on-device learning. By having an integrated preprocessing pipeline, running a model on a device using one of the runtimes is extremely simple and only requires 5 to 15 lines of code, regardless of model or the amount of preprocessing.

The Uniqueness

What differs Ekkono’s technology from other machine learning techniques is the ability to do incremental learning on streaming sensor data – onboard the device. The benefits are significant:

  • No need to collect any data in advance.

  • Our software process high-frequency sensor data in real-time.

  • Data is processed onboard the device, which means product companies don’t have to send raw and potentially sensitive data, only relevant and enriched data, to the cloud for further analysis.

  • Radically reduced need for bandwidth and less dependency on the quality of the network connection.

  • Through incremental learning models adapt to local environments and learn their individual conditions.

  • Small memory footprint. You can even run Ekkono Crystal on a C64!

The Ekkono SDK

  • The software libraries are designed to efficiently provide edge machine learning and streaming analytics capabilities to platforms with constrained resources, with key features such as incremental learning and pipeline support. Seamless deployment from Python to C/C++.

  • Tools designed to guide through the somewhat complex task of selecting and evaluating different machine learning technologies for each individual use case.

  • Documentation such as Release notes, Implementation guide, API documentation and CRISP-DM Refined process documents.

  • Tutorials in Python on how to work with Ekkono Software for various tasks and use cases.

  • Reference implementations including example applications utilizing Edge libraries for various programming languages. 

  • Ekkono Studio – Online interactive environment based on JupyterLab containing tutorials and use case centric examples.

Built for purpose

  • Incremental learning for real-time continuous learning

  • Model-integrated data preprocessing pipeline

  • Conformal predictions

  • Automated change and anomaly detectors

  • Signal processing

  • Small memory footprint

  • No third-party dependencies

  • Hot swapping of models

Machine learning techniques used by Ekkono

  • Linear regression

  • Regression trees

  • Random forest

  • Neural networks

  • Ensembles

Check The Fit

If you've scrolled this far then you're probably far along in your thought-process of implementing Edge AI!

 

Let us help you. No strings attached.

Want to know more? You're welcome to get in touch any time!

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