mlsanityData to 14 Oct 2026

A Multicore and Edge TPU-Accelerated Multimodal TinyML System for Livestock Behavior Recognition

arXiv 2504.11467 ↗First posted 10 Apr 2025cs.CV eess.IVcited by 3

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Abstract (arXiv)

The advancement of technology has revolutionized the agricultural industry, transitioning it from labor-intensive farming practices to automated, AI-powered management systems. In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity. This work presents a novel approach to animal activity recognition and movement tracking, leveraging tiny machine learning (TinyML) techniques, wireless communication framework, and microcontroller platforms to develop an efficient, cost-effective livestock sensing system. It collects and fuses accelerometer data and vision inputs to build a multimodal network for three tasks: image classification, object detection, and behavior recognition. The system is deployed and evaluated on commercial microcontrollers for real-time inference using embedded applications, demonstrating up to 270× model size reduction, less than 80ms response latency, and on-par performance comparable to existing methods. The incorporation of the wireless communication technique allows for seamless data transmission between devices, benefiting use cases in remote locations with poor Internet connectivity. This work delivers a robust, scalable IoT-edge livestock monitoring solution adaptable to diverse farming needs, offering flexibility for future extensions.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 10 Apr 2025.

Builds on

21 of 43 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  7. 2015Fast R-CNNcited by 2,933
  8. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  9. 2019Searching for MobileNetV3cited by 1,994
  10. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  11. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  12. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 414
9 more
  1. 2018AMC: AutoML for Model Compression and Acceleration on Mobile Devicescited by 378
  2. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 273
  3. 2020A Comprehensive Survey of Neural Architecture Search: Challenges and Solutionscited by 254
  4. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 226
  5. 2021MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learningcited by 89
  6. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 55
  7. 2024Tiny Machine Learning: Progress and Futurescited by 40
  8. 2015Object Detection Networks on Convolutional Feature Mapscited by 17
  9. 2025Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 9
  2. 2026Node Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AIcited by 0
  3. 2026Tiny Collaborative Inference for Occlusion-Robust Object Detectioncited by 0