# Circadia: Checkpointing for Intermittent Computing in AI Driven Applications

Matthieu Rodet, Jean-Luc Béchennec, Mikaël Briday, Sébastien Faucou, Isabelle Puaut, Erven Rohou

► To cite this version:

Matthieu Rodet, Jean-Luc Béchennec, Mikaël Briday, Sébastien Faucou, Isabelle Puaut, et al., Circadia: Checkpointing for Intermittent Computing in AI Driven Applications. ACACES 2025 - 21st International Summer School on Advanced Computer Architecture and Compilation for High-performance Embedded Systems, Jul 2025, Salerno, Italy. 2025. [hal-05340610](https://hal.science/hal-05340610v1)

HAL Id: hal-05340610

https://hal.science/hal-05340610v1

Submitted on 31 Oct 2025

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Matthieu Rodet¹
Sébastien Faucou²

Jean-Luc Béchennec²
Isabelle Puaut¹

Mikaël Briday²
Erven Rohou¹

¹ Univ Rennes, Inria, CNRS, IRISA – name.surname@inria.fr
² Nantes Université, École Centrale Nantes, CNRS, LS2N – name.surname@ls2n.fr

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IRISA

## 1. Intermittent Computing

**Battery-less sensors:** Replacing battery by

* energy harvester (solar panel, RF harvester)

* energy buffer ((super-)capacitor)

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**Problem:** frequent and unexpected shutdowns → volatile state reset (register, volatile memory) → progress loss

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**Solution:** Checkpoints to save progress

## 2.1 CNN Architecture

Convolution Neural Networks: sequence of layers

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## 2.2 CNN division

**Where** can we place checkpoints?

→ Layer division into elementary operations:

* **Convolutional layer:** sequence of matrix convolutions (application of a small kernel over a bigger matrix)

* **Pooling:** sequence of matrix local maximum/average

* **Dense:** unique matrix multiplication

## 3. Circadia's Algorithm

### • Inputs

→ elementary operations (OPs)

→ Worst Case Energy Consumption (WCEC) of OPs

→ WCEC of checkpoints (CPs)

→ System's capacitor energy capacity (CAPA)

### • Output

→ Indexes of selected checkpoints

**Execution scheme:** checkpoint = halt to collect energy
**Circadia:** greedy approach, start with full CAPA, decreased for each OP until not enough. Places checkpoint and restart with full CAPA. Example with CAPA=100:

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## 4. Experimental Results

**Baselines:**

* **Schematic:** general-purpose approach

* **Julienning:** energy-optimal CNN-dedicated approach

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* Dedicated algorithms reduce energy spent by 35%

* Circadia is nearly as energy efficient as Juliening

* Circadia is way faster and scalable than Juliening

## Funding Project: ANR-23-IAS3-0003

The authors acknowledge the ANR - FRANCE (French National Research Agency) for its financial support of the OWL project n°ANR-23-IAS3-0003. The **OWL ANR project** proposes a new model of computation for more frugal intelligent autonomous sensors: circadian artificial intelligence. The targeted applications are in the field of environmental monitoring, especially bioacoustic and its application in conservation ecology. This model is well suited for sensors without batteries that are intermittently powered by ambient energy.

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anr