# Ultra Low Power Ambient Artificial Intelligence

Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville

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Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville. Ultra Low Power Ambient Artificial Intelligence. Journées sur la recherche en apprentissage frugal (JRAF), Grenoble INP; Inria; EcoInfo, Nov 2022, Grenoble, France. hal-03986104

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Submitted on 13 Feb 2023

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Distributed under a Creative Commons Attribution 4.0 International License
Ultra Low Power Ambient Artificial
Intelligence

Antoine Bonneau¹,² Frédéric Le Mouël¹ Fabien Mieyeville²

Contact : antoine.bonneau@insa-lyon.fr

¹Univ Lyon, INSA Lyon, Inria, CITI, EA3720, 69621 Villeurbanne, France

²Univ Lyon, Université Claude Bernard Lyon 1, INSA Lyon, Ecole Centrale de Lyon, CNRS, Ampère, UMR5005, 69622 Villeurbanne, France

JRAF - Grenoble - Nov. 2022
1 Use Cases & Context

- Ampere's active control platform

- CITI's Cortex Lab

- Ambient / Frugal AI

- Thesis Subject

2 Research key points / State of the art (SoA)

- On-Device Learning

- Intermittent Learning

- Federated Learning

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

► Issues

► Vibration Reduction

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

► Issues

► reallocate / power off antennas

► max covering / bandwidth / homogeneity

► min interferences
► Ambient Intelligence (Aml)<fnref n="1" />

▶ embedded

▶ context-aware

▶ adaptive

▶ anticipatory

► Frugal Artificial Intelligence (Frugal AI)<fnref n="2" />

► input : less data

► learning process : less computational resources

► model : less memory

★ reduced energy consumption

<footnote n="1">¹E. Aarts [Jan. 2004]. "Ambient intelligence: a multimedia perspective". In: *IEEE Multimedia*. DOI: 10.1109/MMUL.2004.1261101.</footnote>

<footnote n="2">²Mikhail Evchenko et al. [Nov. 2021]. *Frugal Machine Learning*. DOI: 10.48550/arXiv.2111.03731.</footnote>
* Started last month : ~ 20 papers selected

* Goals

* Deployment of a WSN as a monitoring and adaptive action solution

* Minimal footprint energy aiming for autonomy

* 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)
1. Use Cases & Context

- Ampere's active control platform

- CITI's Cortex Lab

- Ambient / Frugal AI

- Thesis Subject

2. Research key points / State of the art (SoA)

- On-Device Learning

- Intermittent Learning

- Federated Learning

3. Work plan
* Target : WSN Nodes → IoT low-end devices [Ojo et al. 2018]

<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>

* Memory constraints → Compressed Models [Profentzas et al. 2022]

* Pruning

* Quantization

* Encoding

* Computation constraints → Preprocessed Models [Lin et al. 2022]

* Transfer Learning

* Fine Tuning
► Target : WSN Nodes → IoT low-end devices [Ojo et al. 2018]

<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>

► Memory constraints → Compressed Models [Profentzas et al. 2022]

► Pruning

► Quantization

► Encoding

Use 2-3 × more RAM

► Computation constraints → Preprocessed Models [Lin et al. 2022]

► Transfer Learning

► Fine Tuning

Tested on IoT middle-end device
No energy consumption evaluation
<figure><img src="image_4.png" /><figcaption>Figure 3: Intermittent Learner duty [Lee et al. 2019]</figcaption></figure>

* Energy Harvesting → Extreme power scarcity

* checkpointing approaches

* task-based approaches [Lee et al. 2019]

* Changes in energy availability → Energy-aware reconfiguration
[Bakar et al. 2021]

* peripheral selection / sensor sampling

* communication pattern
<figure><img src="image_5.png" /><figcaption>Figure 3: Intermittent Learner duty [Lee et al. 2019]</figcaption></figure>

* Energy Harvesting → Extreme power scarcity

* checkpointing approaches

* task-based approaches [Lee et al. 2019]

Do not consider powering off by itself

* Changes in energy availability → Energy-aware reconfiguration
[Bakar et al. 2021]

* peripheral selection / sensor sampling

* communication pattern

Use tiny machine learning model instead of heuristics
<figure><img src="image_6.png" /><figcaption>Figure 4: DFL architectures [Beltrán et al. 2022]</figcaption></figure>

* Learning in a spatially correlated group → Adaptive Neighbor Matching [Z. Li et al. 2022]

* Costly communication → reduce shared weights [C. Li et al. 2021]
<figure><img src="image_7.png" /><figcaption>Figure 4: DFL architectures [Beltrán et al. 2022]</figcaption></figure>

Do not evolve with changes & resource-consuming

► Learning in a spatially correlated group → Adaptive Neighbor Matching [Z. Li et al. 2022]

► Costly communication → reduce shared weights [C. Li et al. 2021]
Use a centralized FL Algorithm
## 1 Use Cases & Context

- Ampere's active control platform

- CITI's Cortex Lab

- Ambient / Frugal AI

- Thesis Subject

## 2 Research key points / State of the art (SoA)

- On-Device Learning

- Intermittent Learning

- Federated Learning

## 3 Work plan
Work plan ... will most likely age poorly

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Ultra Low Power Ambient Artificial
Intelligence

Antoine Bonneau¹,² Frédéric Le Mouël¹ Fabien Mieyeville²

Contact : antoine.bonneau@insa-lyon.fr

¹Univ Lyon, INSA Lyon, Inria, CITI, EA3720, 69621 Villeurbanne, France

²Univ Lyon, Université Claude Bernard Lyon 1, INSA Lyon, Ecole Centrale de Lyon, CNRS, Ampère, UMR5005, 69622 Villeurbanne, France

JRAF - Grenoble - Nov. 2022
<figure><img src="image_32.png" /></figure>

Bakar, Abu et al. (Sept. 2021). "REHASH: A Flexible, Developer Focused, Heuristic Adaptation Platform for Intermittently Powered Computing". In: *Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.* DOI: 10.1145/3478077.

<figure><img src="image_33.png" /></figure>

Beltrán, Enrique Tomás Martínez et al. (Nov. 2022). *Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges*. DOI: 10.48550/arXiv.2211.08413.

<figure><img src="image_34.png" /></figure>

Lee, Seulki et al. (Dec. 2019). "Intermittent Learning: On-Device Machine Learning on Intermittently Powered System". In: *Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.* DOI: 10.1145/3369837.

<figure><img src="image_35.png" /></figure>

Li, Chengxi et al. (Oct. 2021). "Communication-Efficient Federated Learning Based on Compressed Sensing". In: *IEEE Internet Things J.* DOI: 10.1109/JIOT.2021.3073112.
<figure><img src="image_36.png" /></figure>

Li, Zexi et al. (Nov. 2022). "Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching". In: *IEEE Transactions on Big Data*. DOI: 10.1109/TBDATA.2022.3222971.

<figure><img src="image_37.png" /></figure>

Lin, Ji et al. (July 2022). "On-Device Training Under 256KB Memory". In: DOI: 10.48550/arXiv.2206.15472.

<figure><img src="image_38.png" /></figure>

Ojo, Mike O. et al. (2018). "A Review of Low-End, Middle-End, and High-End IoT Devices". In: *IEEE Access*. DOI: 10.1109/ACCESS.2018.2879615.

<figure><img src="image_39.png" /></figure>

Profentzas, Christos et al. (Sept. 2022). "MiniLearn: On-Device Learning for Low-Power IoT Devices". In: *International Conference on Embedded Wireless Systems and Networks*. URL: https://research.chalmers.se/en/publication/531933.