# Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

arXiv: https://arxiv.org/abs/2608.03589 · HTML: https://mlsanity.com/paper/2608.03589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model](https://mlsanity.com/paper/2210.01625/) (2022)
- [MicroNAS: Zero-Shot Neural Architecture Search for MCUs](https://mlsanity.com/paper/2401.08996/) (2024)
- [On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers](https://mlsanity.com/paper/2407.10734/) (2024)

## Abstract (arXiv)

We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). In mobile applications where the energy can run out, e.g., when solar-powered, executing artificial intelligence (AI) faces a technical issue as learning can be interrupted at any time. Our approach combines a hardware-aware energy prediction model with multi-objective optimization (MOO), enabling offline DNN optimization at the design stage without repeated deployment and online testing on the target MCU. Our proposed energy predictor estimates per-layer energy consumption for both DNN inference and training, including the intermittent checkpointing overhead, based on implementation-specific compute and memory features extracted from the DNN model. We validate our approach using autoencoders for anomaly detection on a Cortex-M4 MCU, where our predictor achieves a weighted absolute percentage error of 16.6%, which is sufficient for reliable architecture selection under intermittency constraints. As a result, this work bridges the gap between MOO, automated DNN design, deployment on energy-harvesting systems, and intermittent learning, truly enabling autonomous AI at the edge.