mlsanityData to 7 Oct 2026

Edge Impulse: An MLOps Platform for Tiny Machine Learning

arXiv 2212.03332 ↗First posted 2 Nov 2022cs.DC cs.LG cs.SEcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 16 of its 66 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

16 of 66 references.

References · 16

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  3. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  4. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  5. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  6. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 269
  7. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
  8. 2021MLPerf Tiny Benchmarkcited by 118
  9. 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116
  10. 2022DataPerf: Benchmarks for Data-Centric AI Developmentcited by 89
  11. 2019Visual Wake Words Datasetcited by 46
  12. 2019ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detectioncited by 43
4 more
  1. 2021Widening Access to Applied Machine Learning with TinyMLcited by 17
  2. 2017A Cascade Architecture for Keyword Spotting on Mobile Devicescited by 15
  3. 2022CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAscited by 11
  4. 2022TinyMLOps: Operational Challenges for Widespread Edge AI Adoptioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 12; 2026: 4.

24 papers.

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  1. 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
  2. 2023Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Reviewcited by 9
  3. 2024On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AIcited by 7
  4. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  5. 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applicationscited by 6
  6. 2024On-device Online Learning and Semantic Management of TinyML Systemscited by 5
  7. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5
  8. 2023Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollerscited by 4
  9. 2025Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?cited by 3
  10. 2026Future of Edge AI in biodiversity monitoringcited by 3
  11. 2023REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraintscited by 2
  12. 2025Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyMLcited by 2
12 more
  1. 2025TinyML for Speech Recognitioncited by 2
  2. 2024Realtime Person Identification via Gait Analysiscited by 1
  3. 2025The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Educationcited by 1
  4. 2025Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Modelscited by 1
  5. 2025Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutionscited by 1
  6. 2024Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systemscited by 0
  7. 2025Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysiscited by 0
  8. 2025Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)cited by 0
  9. 2025When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learningcited by 0
  10. 2026Feasible-First Exploration for Constrained ML Deployment Optimization in Crash-Prone Hierarchical Search Spacescited by 0
  11. 2026CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systemscited by 0
  12. 2026Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languagescited by 0