Experiment 3: Lightweight Neurons for Efficient MNIST Classification

Exploring lightweight neural architectures for MNIST that dramatically reduce resource consumption while maintaining high accuracy.

Experiment 3: Lightweight Neurons for Efficient MNIST Classification

Introduction

Building on the momentum of our previous experiments and the broader research landscape, Experiment 3 set out to answer a pressing question: Can we design neural architectures that dramatically reduce resource consumption while maintaining competitive accuracy on MNIST? With the proliferation of edge devices and the growing need for sustainable AI, lightweight models are more relevant than ever.

Motivation

Our earlier work (see Experiments 1 and 2) explored classic and modern architectures, as well as the practicalities of integrating coding assistants into the research workflow. However, the challenge of balancing model efficiency and performance remained. Inspired by recent advances in neural architecture search and the push for green AI, we hypothesized that a carefully engineered set of lightweight neurons could close the gap between resource usage and accuracy.

Methodology

Consistent with our config-driven philosophy, all experiment parameters were defined in JSON configs, ensuring reproducibility and transparency. The core pipeline leveraged our modular framework:

• Config Management: All model, dataset, and resource parameters were loaded and validated via core/config_manager.py.
• Model Instantiation: Models were dynamically constructed from config using core/model_factory.py, with new lightweight variants implemented in models/lightweight.py.
• Training & Evaluation: The core/experiment_runner.py orchestrated training, validation, and reporting, with resource constraints enforced for fair comparison.
• Profiling: We used utils/model_resource_profiler.py to quantify parameter counts, memory, and storage for each model.

All experiments were run on the MNIST dataset, with results, metrics, and plots saved to the results/ directory. Error handling and logging followed our robust patterns established in previous work.

Results

The lightweight models delivered on their promise:

• Parameter Count: Reduced by up to 80% compared to traditional CNNs, with some variants under 50k parameters.
• Accuracy: Top lightweight models achieved >98% test accuracy, within 0.5% of the best full-sized baselines.
• Resource Usage: Memory and storage footprints were slashed, making these models ideal for deployment on constrained hardware.
• Training Time: Faster convergence and lower energy consumption were observed, as profiled by our resource monitoring tools.

Detailed results, including model rankings and ablation studies, are available in the results/ subdirectory. CSVs and summary reports provide a transparent view of each model’s trade-offs.

Discussion

This experiment validates the hypothesis that lightweight neural architectures can deliver state-of-the-art performance on MNIST with a fraction of the resources. The config-driven approach enabled rapid iteration and fair benchmarking, while our error handling and profiling tools ensured reliability and insight.

Notably, the integration of coding assistants (see “Three Weeks with Coding Assistants”) accelerated development and debugging, allowing us to focus on research questions rather than boilerplate. The synergy between automation and human expertise was a recurring theme throughout this project.

Conclusion & Next Steps

Experiment 3 demonstrates that efficient AI is not only possible but practical. Our lightweight models are ready for real-world deployment, and the framework is primed for extension to more complex datasets and tasks. Future work will explore:

• Transferability to other domains (e.g., CIFAR, speech)
• Automated hyperparameter tuning
• Further reductions in energy and memory usage

As always, all code, configs, and results are open for review and reuse. We invite the community to build on these findings and push the boundaries of efficient AI.

For more details, see the full experiment logs and reports in the repository. Feedback and collaboration are welcome!