Optimizing MNIST Digit Recognition for Edge Devices: A Multi-dimensional Evaluation Approach

Abstract

Handwritten digit recognition on the MNIST dataset routinely achieves near-perfect accuracy. However, deploying these models on edge platforms introduces new challenges where predictive accuracy is no longer the only metric that matters. For constrained systems, memory footprint, computational overhead, and inference latency are equally critical. In this work, they propose a multi-dimensional evaluation framework for deployment-oriented assessment. The authors introduce the Edge Suitability Score (ESS), a composite metric that combines normalized accuracy, model size, and inference speed into a single value, weighted at 0.40, 0.35, and 0.25, respectively, to reflect their relative importance for microcontroller deployment. By comparing two lightweight architectures, a scaled-down CNN (L-CNN) and a depthwise-separable L-MobileNet, against a deeper Baseline CNN, the results show that compact networks can maintain near-99% accuracy while drastically reducing storage and computation requirements: L-MobileNet achieves 99.10% accuracy with only 12,186 parameters and roughly 48 KB of weight memory, compared with 99.45% accuracy and over 1 MB for the baseline. This framework offers a practical methodology for selecting neural networks in real-world edge environments, bridging the gap between theoretical performance and actual deployability on resource-constrained hardware such as the STM32 and ESP32.