Meet EON™

December 9, 2020

Meet EON™, a new Edge Optimized Neural (EON™) compiler from Edge Impulse that will kick your TinyML code into overdrive. EON™ runs a neural network in 25-55% less RAM and up to 35% less flash, while retaining the same accuracy, compared to TensorFlow Lite for Microcontrollers. EON™ achieves this magic by compiling your neural networks to C++, unlike other embedded solutions using generic interpreters, thus eliminating complex code, device power, and precious time.

The Industry's Fastest Neural Compiler by Edge Impulse 

9AM PT | 12PM ET | 6PM CET

In this webinar you'll get a peek at the capabilities of this new neural complier and a chance to see it in action during a live demo with Edge Impulse.

Edge Impulse is giving away an Oura Ring to one lucky webinar participant! Register and attend the event online, December 9th to get your name in the drawing to win this giveaway.

Special Offer!

Oura Ring Giveaway

Duration: 1 Hour

45-minute Presentation

15-minute Q&A

  • What is EON™ and why was it built
  • How to compile neural networks to C++ in record speed and (smaller) size

What you will learn

From getting started, to MLOps in production, Edge Impulse provides maximum efficiency on a wide range of hardware from MCUs to CPUs thanks to Edge Optimized Neural (EON)™ technology.

Plus, watch a live demonstration of EON™ in action!


Jan Jongboom

Co-Founder and CTO, Edge Impulse

Jan is an open source developer and technology evangelist working on TinyML as the CTO of Edge Impulse. Over the past years he has gone up and down the stack, from embedded to web, and everything in between. Jan is a strong believer of open source, and has written thousands of patches for open source projects, including Mozilla Firefox OS, JerryScript (embedded JS runtime), Arm Mbed.

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Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises.

Edge Impulse was designed for software developers, engineers and domain experts to solve real problems using machine learning on edge devices without a PhD in machine learning.

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