# Ultra Low Power Ambient Artificial Intelligence

Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville

► To cite this version:

Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville. Ultra Low Power Ambient Artificial Intelligence. Journées non thématiques du GDR RSD, Jan 2023, Villeurbanne, France. hal-03986137

HAL Id: hal-03986137

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Submitted on 13 Feb 2023

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Distributed under a Creative Commons Attribution 4.0 International License
## Abstract

The increase in autonomy of ambient intelligence devices has led to the evolution of Internet of Things (IoT) towards the notion of Edge Computing and is driving Wireless Sensor Networks (WSN) (considered as the Low End Devices of IoT) towards total energy autonomy by recovering ambient energy. While the majority of current Machine Learning applications are still centralized in Clouds, we could take advantage of those frugal developments to propose systems that take into account their hardware and energy limitations for collaborative and adaptive distributed learning.

## Thesis Goals

* Deployment of a WSN as a monitoring and adaptive action solution
* Minimal footprint energy aiming for autonomy (battery-less)
* Exploiting networking AI to improve and maintain itself
* Energy efficiency as a cross-cutting topic
* Focus on Time Series data (reduced feature set and samples)

## Ampere's active control platform

<figure><img src="image_2.png" /><figcaption>Figure 1: Ampere's vibrating bench</figcaption></figure>

* Experimental Issues
* Low computational power but High Energy Harvesting (EH) potential
* Reduce vibration under a threshold locally, collaboratively and globally
* Keep WSN up through piezoelectric EH and energy balancing

## CITI's Cortex Lab

<figure><img src="image_3.png" /><figcaption>Figure 2: Nodes and activation example in Cortex Lab platform</figcaption></figure>

* Experimental Issues
* High computational power but Low EH potential
* Modify antennas behaviour (reallocation, beamforming, power off, ...)
* Maximize covering, bandwidth, signal homogeneity, ...
* Minimize interferences

## On-Device Learning: Resources constrained learning

* Memory constraints → Compressed Models [1]
* Pruning (adaptative, distributed, ...)
* Quantization (aware training, partial, ...)
* Huffman Encoding.
* Computation constraints → Preprocessed Models [2]
* Use pre-trained models on boards.
* Sparsely fine tune model depending on the model.

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## Target: WSN Nodes / IoT low-end devices

<table><thead><tr><th>MCU Arch</th><th>Clock speed</th><th>On-Board Storage</th><th>RAM</th><th>Current</th></tr></thead><tbody><tr><td>16 or 32 bit</td><td>≤ 50 MHz</td><td>≤ 512 KB</td><td>≤ 64 KB</td><td>≤ 10 mA</td></tr></tbody></table>

## Intermittent Learning: Duty Cycling

<figure><img src="image_5.png" /><figcaption>Figure 3: Intermittent Learner duty [3]</figcaption></figure>

* Extreme power scarcity → Energy Harvesting
* Save computations through gradient / energy-aware checkpointing
* Decompose monolithic ML with task-based approaches [3]
* Changes in energy availability → Energy-aware reconfiguration [4]
* Power on / off different peripherals and change sensors sampling rates
* Adapt compute complexity through early exiting
* Change focus from learning to inference

## Federated Learning: Bypass computational weaknesses

<figure><img src="image_6.png" /><figcaption>Figure 4: DFL architectures [5]</figcaption></figure>

* Learning in a spatially correlated group → Adaptive Neighbor Matching
* Select neighbors based on similar performances (gossip and PENS)
* Establish selection that can significantly improve convergence speed and training loss.
* Communication is energy-consuming → Optimize transmission
* Reduce shared model size with compressed sensing [6]
* Choosing communication-efficient aggregators (e.g Dynamic Average Consensus-Based)

## Perspectives

* Build a simulator on top of ns3 to evaluate communication energy consumption of Federated Learning for different topologies.
* Varying microcontrollers to determine how low you can go: Raspberry Pi, STM32 F302R8, STEVAL-STLK01, MSP430
* Plugging different types of Energy Harvesting modules (Solar, RF, piezoelectric) and adapting intermittent schedulers to federated learning.
* Comparing energy consumption to traditional AI approaches (ARIMA)

## References

[1] C. Profentzas, M. Almgren, and O. Landsiedel, "MiniLearn: On-Device Learning for Low-Power IoT Devices," presented at the International Conference on Embedded Wireless Systems and Networks, Sep. 2022. [Online]. Available: https://research.chalmers.se/en/publication/531933.
[2] J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, C. Gan, and S. Han, "On-Device Training Under 256KB Memory," Jul. 2022. DOI: 10.48550/arXiv.2206.15472.
[3] S. Lee, B. Islam, Y. Luo, and S. Nirjon, "Intermittent Learning: On-Device Machine Learning on Intermittently Powered System," Proceedings ACM Interactive Mobile Wearable Ubiquitous Technologies, Dec. 2019. DOI: 10.1145/3369837.
[4] A. Bakar, A. G. Ross, K. S. Yildirim, and J. Hester, "REHASH: A Flexible, Developer Focused, Heuristic Adaptation Platform for Intermittently Powered Computing," Proceedings ACM Interactive Mobile Wearable Ubiquitous Technologies, Sep. 2021. DOI: 10.1145/3478077.
[5] E. T. M. Beltrán, M. Q. Pérez, P. M. S. Sánchez, et al., "Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges," Nov. 2022. DOI: 10.48550/arXiv.2211.08413.
[6] C. Li, G. Li, and P. K. Varshney, "Communication-Efficient Federated Learning Based on Compressed Sensing," IEEE Internet Things J., Oct. 2021. DOI: 10.1109/JIOT.2021.3073112.

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