Md Salim, Sani Irwan and Mohd Shaifullizan, Muhammad Aiman Akmal and Mohd Zin, Mohd Shahril Izuan and Samsudin, Sharatul Izah and Awang Md Isa, Azmi (2025) Performance evaluation of image classification models on resource-constrained STM32 microcontrollers. International Journal of Research and Innovation in Social Science (IJRISS), IX (X). pp. 2977-2986. ISSN 2454-6186
![[img]](http://eprints.utem.edu.my/style/images/fileicons/text.png) |
Text
003291501202612022919.pdf Download (725kB) |
Abstract
Deploying deep learning on microcontrollers offers real-time intelligence at the edge, but tight memory and compute budgets complicate design choices. This study evaluates image classification on the STM32H747IDISCO using a compact convolutional neural network trained on five board classes (Arduino Uno, Node MCU, ESP8266-01, Micro: bit V2.0, ESP32-CAM). A small, augmented dataset (50–100 images per class) was used with standard transformations; models were quantised to int8 and deployed via STM32CubeIDE and the STM32- AI CLI. The analysis examines how input resolution (1080p vs 480p) interacts with accuracy, memory footprint, latency, and power. Four classes achieve ≥95% accuracy across both resolutions, while ESP8266-01 improves from 65.7% (1080p) to 92.3% (480p), suggesting that downsampling can suppress distracting fine-grained artefacts. Activation-buffer tuning and post-training quantisation reduce RAM from ~761 kB to ~610 kB and Flash from ~1.42 MB to ~1.20 MB without accuracy loss; 480p further lowers latency by up to 35% and power by ~20%. The findings provide a resolution-aware benchmark and practical guidance for balancing fidelity and efficiency on STM32-class MCUs, and they motivate future work with larger benchmarks, cross-platform comparisons, and pruning/distillation pipelines.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | STM32H747I-DISCO, Tiny ML, Edge AI, Image classification, Model optimisation |
| Divisions: | Faculty Of Electronics And Computer Technology And Engineering |
| Depositing User: | Sabariah Ismail |
| Date Deposited: | 23 Feb 2026 01:23 |
| Last Modified: | 23 Feb 2026 01:23 |
| URI: | http://eprints.utem.edu.my/id/eprint/29506 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |