The text discusses the implementation of an MNIST inference engine on a low-end Padauk 8-bit microcontroller, specifically the PMS150C, demonstrating the feasibility of neural networks under extreme memory constraints. It explores how reducing image resolution and tweaking model architecture allows for satisfactory test accuracy while maintaining minimal memory usage. Surprisingly, an impressive accuracy of over 90% was achieved with a model of only 0.414 kilobytes. This work illustrates the potential for machine learning applications even in very limited computing environments, despite challenges in practical usability.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Efficient ML on ultra-low-cost microcontrollers | From high-cost to ultra-low-cost ML solutions | Widespread ML applications in microcontroller tech | Demand for low-cost, efficient computing |
| Achieving high accuracy with minimal resources | From resource-intensive to resource-efficient ML models | Advanced applications on even simpler devices | Miniaturization of technology |
| Simplification of ML for embedded systems | From complex frameworks to simplified, targeted solutions | Robust ML solutions in hyper-optimized hardware | Need for faster and more efficient ML deployment |
| Exploring lower limits of machine learning implementations | From theoretical limits to practical applications | Routine ML training and inference on cheap devices | Continuous innovation in machine learning techniques |
| Application of assembly code in ML on MCUs | From high-level programming to low-level optimization | New programming paradigms tailored to efficient computing | Need for maximizing performance on limited hardware |