IoT and Edge Computing using virtualized low-resource integer Machine Learning with support for CNN, ANN, and Decision Trees
Stefan Bosse · Annals of Computer Science and Information Systems · 2023
Data-driven models used for predictive classification and regression tasks are commonly computed using floating point arithmetic preserving accuracy by automatic scaling even in high non-linear functions.With respect to distributed sensor networks like the IoT, sensor data is acquired on low-resource embedded systems and delivered to data servers characterized by big data volumes.In specific use cases and domains, local predictive modelling on low-power devices is desired or required.But heterogeneity of host platforms and dynamic programming disables machine code deployment.This work addresses Tiny ML on very lowresource devices (microcontrollers, less than 32 kB RAM and ROM) by using a stack-based Tiny Virtual Machine providing core ML operations to implement Decision Trees (DT), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN).VM program code is always provided in textual format and compiled just-in-time to Bytecode to ensure portability, servicability, and mobility.Two damage diagnostics use-cases demonstrate the suitability of the VM approach, and even time consuming computational tasks do not compromise the overall responsiveness of the platform by using a real-time approach.This work addresses the underlying integer arithmetic operations required to implement efficient and fast computable ML models on microncontrollers.