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Edge Impulse: An MLOps Platform for Tiny Machine Learning

Authors :
Hymel, Shawn
Banbury, Colby
Situnayake, Daniel
Elium, Alex
Ward, Carl
Kelcey, Mat
Baaijens, Mathijs
Majchrzycki, Mateusz
Plunkett, Jenny
Tischler, David
Grande, Alessandro
Moreau, Louis
Maslov, Dmitry
Beavis, Artie
Jongboom, Jan
Reddi, Vijay Janapa
Publication Year :
2022

Abstract

Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizations difficult and unportable. We present Edge Impulse, a practical MLOps platform for developing TinyML systems at scale. Edge Impulse addresses these challenges and streamlines the TinyML design cycle by supporting various software and hardware optimizations to create an extensible and portable software stack for a multitude of embedded systems. As of Oct. 2022, Edge Impulse hosts 118,185 projects from 50,953 developers.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2212.03332
Document Type :
Working Paper