Welcome to the EmbeddedML-Benchmark results page. Explore the performance metrics of various machine learning models optimized for embedded systems below. The results are based on real-world benchmarks for different ML tasks: Keyword Spotting, Image Classification, Anomaly Detection, and Emotion Detection.
Benchmark Results
| Use Case | Dataset | TFLite Model Size | Quality Target |
|---|---|---|---|
| Image Classification | CIFAR 10 (32x32) | TinyML (9 KB) | 85% (Top-1) |
| Keyword Spotting | Speech Communication (49x10) | Dense-CNN (53 KB) | 89% (Top-1) |
| Anomaly Detection | ToyADMOS (5x128) | FC-AutoEncoder (41 KB) | 99% (Top-1) |
| Emotion Detection | Multi-Class Emotional Sentences (298x12) | LSTM (161 KB) | 89% (Top-1) |
Performance Comparison
Anomaly Detection Results
Emotion Detection Results
Impact of the Benchmark
This benchmarking framework helps to evaluate the deployment of machine learning models in TinyML applications, offering insight into the trade-offs between accuracy, execution speed, and memory efficiency. It will assist researchers and engineers in choosing the most effective models and optimizations for real-time embedded systems.