Build Real-Time Classifiers on Microcontrollers with TinySVMEmbedded machine learning is no longer science fiction — tiny devices at the edge can classify sensor data, detect anomalies, and run simple decision systems without cloud connectivity. This article explains how to build real-time classifiers on microcontrollers using TinySVM: what TinySVM is, why it fits constrained hardware, how to train and compress models, strategies for deployment, and practical tips for achieving reliable, low-latency inference.
What is TinySVM?
TinySVM is a compact implementation of Support Vector Machines (SVMs) tailored for resource-constrained environments such as microcontrollers and other embedded systems. Unlike full-featured SVM libraries that prioritize flexibility and extensive kernel support, TinySVM focuses on minimal memory footprint, predictable execution time, and low computational overhead, making SVMs feasible on devices with kilobytes of RAM and modest CPU frequency.
Why use SVMs on microcontrollers?
- Deterministic inference: SVM decision functions are simple dot-products plus bias for linear SVMs, producing predictable runtime and latency — vital for real-time systems.
- Good performance with small datasets: SVMs generalize well when labeled data are limited.
- Compact models possible: With linear SVMs or sparse support vectors, model sizes can be tiny compared to deep neural networks.
- Interpretable decision boundaries: Easier debugging and verification in safety-sensitive embedded applications.
Resource constraints and design choices
Microcontrollers impose strict limits on RAM, flash, and CPU cycles. Typical considerations when using TinySVM:
- Model type: prefer linear SVMs or very small kernel SVMs (e.g., low-degree polynomial or approximated RBF).
- Data dimensionality: reduce input features via feature engineering or dimensionality reduction (PCA, feature selection).
- Quantization: store weights and inputs in fixed-point (int8/16) instead of float32.
- Sparse representation: store only non-zero support vectors/weights.
- Incremental inference: process streaming inputs in small windows to avoid buffering large datasets.
Training pipeline (off-device)
Training is done on a desktop or server; the microcontroller receives a lightweight model artifact.
- Collect and label dataset representative of on-device conditions (sensor noise, sampling rates, environment).
- Preprocess: normalize, remove outliers, and extract features (time-domain: mean, RMS, zero-crossings; frequency-domain: low-frequency band energies).
- Choose model:
- Linear SVM for simplest, smallest footprint.
- Kernel SVM only if needed for accuracy; consider kernel approximation (e.g., Random Fourier Features).
- Train and validate with cross-validation; measure performance under simulated embedded noise.
- Compress: prune small weights/support vectors, quantize parameters (8-bit/16-bit), and convert to fixed-point representation.
- Export model file with metadata: feature scaling parameters, quantization scale/zero-point, and inference order.
Example model export format
A compact model file should include:
- Model type: linear/kernel
- Number of classes and class labels
- Weight vector(s) (quantized) and bias(es)
- Feature scaling parameters (mean, std or min/max)
- If kernel SVM: support vectors (quantized) and coefficients or kernel approximation parameters
Representations commonly used: tiny binary blobs, C header arrays (for direct compile-time inclusion), or small filesystem files.
Inference on the microcontroller
Key steps implemented in TinySVM runtime:
- Read raw sensor input and buffer the required window.
- Apply preprocessing using the same scaling parameters used during training. Use integer arithmetic where possible.
- Compute decision function:
- Linear SVM: compute dot(w, x) + b.
- Kernel SVM: compute sum(alpha_i * K(x, sv_i)) + b — expensive; avoid unless necessary.
- Apply class decision rule (sign for binary, one-vs-rest or one-vs-one for multiclass).
- Optionally apply temporal smoothing (moving majority vote) to reduce jitter.
Performance tips:
- Precompute feature extraction incremental updates (sliding window sums) to avoid recomputing from scratch.
- Use DSP instructions (ARM CMSIS-DSP) for fixed-point multiply-accumulate when available.
- Align data in memory and use 32-bit loads when possible to reduce cycles.
Memory and latency optimization techniques
- Quantize to int8: reduces storage and enables SIMD-friendly operations.
- Use fixed-point arithmetic: define Q-format (e.g., Q15) and keep consistent scaling.
- Reduce feature dimension: remove redundant features, use feature hashing or PCA to compress input size.
- Prune weights/support vectors below a threshold to shrink compute and storage.
- Merge scaling into weight/bias: combine normalization with weight values so preprocessing can be reduced to simple integer shifts.
Example: combine normalization x’ = (x – mu)/sigma into weight w’ = w/sigma and bias b’ = b – sum(mu * w’) to remove per-feature division at runtime.
Real-time considerations and evaluation
- Latency budget: define maximum allowable inference time (e.g., 5 ms). Measure dot-product time and feature extraction separately.
- Jitter: use fixed workload (avoid dynamic memory allocation) and ensure deterministic loops.
- Throughput: consider sample rate and whether processing must be done per-sample or per-window.
- Power: lower CPU frequency during idle; burst to full speed for inference if MCU supports dynamic frequency scaling.
Testing:
- Unit-test inference outputs against desktop reference after quantization.
- Run long-duration tests with live sensors to detect drift, thermal effects, and memory fragmentation.
- Measure energy per inference with power-profiling tools.
Example deployment flow (concrete)
- Train linear SVM in Python (scikit-learn).
- Export weight vector and bias, quantize to int16 with known scale.
- Create C header with arrays:
// model.h #include <stdint.h> #define N_FEATURES 32 extern const int16_t svm_weights[N_FEATURES]; extern const int32_t svm_bias; // scaled - Implement inference using Q-format multiply-accumulate and compare against threshold.
- Integrate into firmware, test on hardware, iterate on feature engineering.
Practical use cases
- Vibration-based anomaly detection on motors.
- Wake-word or small-speech command detection from lightweight audio features.
- Simple gesture recognition from accelerometer/gyroscope.
- Environmental classification (e.g., occupancy vs. empty room) using low-rate sensors.
Limitations and when not to use TinySVM
- Very high-dimensional inputs (raw images): deep learning is more appropriate.
- Extremely non-linear boundaries that require many support vectors — model size may blow up.
- Tasks needing end-to-end feature learning from raw data (e.g., complex audio or vision tasks) benefit from learned feature extractors.
Final checklist before shipping
- Validate accuracy on on-device data.
- Confirm quantized inference matches reference within acceptable tolerance.
- Ensure deterministic timing and acceptable memory usage.
- Implement fallbacks or safe states for classifier uncertainty.
- Document model scaling and update procedure for field updates.
TinySVM brings the advantages of SVMs to resource-limited hardware by emphasizing simplicity, compactness, and deterministic behavior. With careful feature engineering, quantization, and attention to inference efficiency, you can run reliable, real-time classifiers on microcontrollers for a wide range of practical embedded applications.
Leave a Reply