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Edge Impulse, the startup offering an end-to-end developer platform for machine learning (ML) on embedded systems, has raised $15 million in a Series A round of funding. The round was led by Canaan Partners with Acrew Capital, Fika Ventures, Momenta Ventures and Knollwood Investment Advisory.
Edge Impulse said this funding will support its mission to democratize ML with an ML development platform that enables embedded engineers to build machine learning systems, handling everything from ML algorithm development and training through management of proprietary datasets.
Edge Impulse has a growing list of silicon partners which today includes Arm, Nordic Semiconductor, Nvidia, Silicon Labs, ST, Microchip, Arduino, Raspberry Pi, Himax and Eta Compute. To date, Edge Impulseâs platform has been used to produce more than 23,000 projects and 26 million labelled data samples, according to the company. These projects span industrial, infrastructure, wearables and even less familiar applications such as wildlife conservation.
Realizing that to use existing ML tooling, one had to be a data science expert, a machine learning algorithm expert, an embedded expert and a compiler expert all in one, Edge Impulseâs co-founders identified an opportunity to build a toolchain specifically for embedded machine learning. This toolchain had to be able to scale as machine learning becomes commonplace for millions of embedded developers. The problem is compounded when we consider that most embedded applications today are using specialized data, typically their own proprietary data sets, which need to be created and managed.
âWe wanted to solve the problems that normal developers and engineers working on edge compute and applying machine learning are going to face. How can we overcome those? How can we make this a successful process to make use of ML? And then how can we make sure they can deploy it in their real jobs in the industry?â said Edge Impulse co-founder and CEO Zach Shelby, in a previous interview with EE Times.
Edge Impulse aims to help engineers with this whole process. A substantial part of the ML development platform is focused on manipulation of the datasets: visualization, analysis, moving datasets around, adding to them. There is also a library of many algorithms, all extensible and customizable, and the process of applying the data to the algorithms is automated. This includes infrastructure for training algorithms and visualizing what is happening. Training and deployment are also supported, with deployment focusing on microcontrollers and low-end CPUs.
Edge Impulseâs business model is a typical software as a service (SaaS) model with a generous free tier for individual engineers. Larger enterprises pay for a subscription, which includes features for collaboration between teams of engineers, working with bigger datasets, model versioning and security.
Sally Ward-Foxton covers AI technology and related issues for EETimes.com and all aspects of the European industry for EETimes Europe magazine. Sally has spent more than 15 years writing about the electronics industry from London, UK. She has written for Electronic Design, ECN, Electronic Specifier: Design, Components in Electronics, and many more. She holds a Masters' degree in Electrical and Electronic Engineering from the University of Cambridge.
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