neural networks distilled into if-else rules for microcontrollers
A knowledge distillation framework that extracts lightweight, interpretable decision rules from trained neural networks, converting black-box models into portable if-else classifiers ready for resource-constrained deployment, with near-equivalent accuracy at a fraction of the cost.
Experimental Results
Evaluated on a balanced IEEE dataset of 14,000 images (7,000 pristine + 7,000 modified)
Inference Speedup
4.56x Faster than NNMemory Reduction
54% Less Memory UsedFidelity to NN
93.52% Decision Match RateDetailed Results
Classification Accuracy
Inference Speed (per sample)
Memory per Inference
Model Size on Disk
Beyond Steganalysis
This research validates the framework on LSB steganalysis as a proof of concept. However, the distillation pipeline is application-agnostic by design—it operates on feature vectors and learned decision boundaries, not domain-specific assumptions. This makes it a promising candidate for any classification task where deploying neural networks on constrained hardware is impractical. Formal evaluation on additional domains remains future work.
Potential Applications
Digital Forensics
Deploy fast, interpretable steganalysis classifiers on forensic workstations and edge devices where explainability and auditability are critical requirements.
Edge & IoT Devices
Run ML classifiers on microcontrollers and embedded systems with extremely limited memory and compute, without any framework dependencies.
Explainable AI
Convert opaque neural network decisions into human-readable if-else rules for domains where transparency and regulatory compliance are non-negotiable.
Real-Time Classification
Achieve sub-millisecond inference times for latency-sensitive classification tasks where every fraction of a millisecond counts.