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Edge Impulse: An MLOps Platform for Tiny Machine Learning

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arxiv 2212.03332 v3 pith:75RYMDDJ submitted 2022-11-02 cs.DC cs.LGcs.SE

Shawn Hymel , Colby Banbury , Daniel Situnayake , Alex Elium , Carl Ward , Mat Kelcey , Mathijs Baaijens , Mateusz Majchrzycki
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Jenny Plunkett David Tischler Alessandro Grande Louis Moreau Dmitry Maslov Artie Beavis Jan Jongboom Vijay Janapa Reddi
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classification cs.DCcs.LGcs.SE
keywords edgeimpulsetinymlhardwaremlopsplatformsoftwaresystems
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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.

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  1. Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)

    cs.SE 2025-09 conditional novelty 3.0 of 10

    Measured latency, CPU, memory, and confidence for three TensorFlow Lite models on BeagleBone AI64 and Raspberry Pi 4; the Raspberry Pi 4 was faster and more resource-efficient in every test.

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