## 1 Introduction
In [9], the computation of complex and deep convolutional neural networks (CNNs) was implemented on the sensor node level, often equipped with digital signal processors (DSPs) with optimized vector operations and sometimes floating-point arithmetic units (FPUs). The authors chose an Arm Cortex M4-based micro-controller, which provides dedicated DSP and FPU operations and 320 kB of RAM. In [3], the authors presented a software framework to run lightweight neural networks on micro-controllers based on both the ARM Cortex-M series and the RISC-V-based parallel ultra-low-power (PULP) platform, especially addressing energy-efficient computing, which is a high constraint on self-powered autonomous sensor nodes. The efficient implementation of ML models using a dedicated model and training library Fast ANN on the Arm Cortex M architecture is also demonstrated in [10]. They claim that the model computation is still possible on FPU-less micro-controllers but do not give evaluations for real use cases. In addition, all these frameworks create static model code, which cannot easily be updated at run-time (no service). Instead of performing software model transformations to map a model using arithmetic A defined by an accuracy, value, and dynamic range, on a model using arithmetic B with lower accuracy and reduced value and dynamic ranges, the model can be mapped on a dedicated hardware architecture, providing the elementary core operations of ML models, as described by [1]. Although this is the most efficient method, this approach prevents the use of widely used and cheap electronic components and universal and flexible model computations, as well as update services (of the model and the ML services). This compromises the deployment of the proposed ML/VM architecture in embedded systems with limited or no reprogramming capabilities (like material-integrated systems, discussed in Section 2). This paper is organized as follows. A short introduction to material-integrated sensor nodes for structural health monitoring (SHM) is given to outline the motivation as well as the communication architecture, defining constraints for the VM and its ML service introduced and used in this work. An extended section describes the REXA VM with a focus on ML. The transformation process of continuous ML models to discretized integer-scaled arithmetic models is described in detail, and approximation methodologies and discretization errors are discussed for non-linear transfer functions. Two use cases demonstrate the capability of the transformation process, the REXA VM ML operations, and typical accuracy losses that can be expected in real applications for regression and classification tasks. We introduce two different model scaling algorithms (static and dynamic) and evaluate the effect of discretization on the model accuracy with the two use-case examples. One use case uses synthetic sensor data created by wave propagation simulation, and the second uses input data from a mathematical model. The first use case combines classification and regression tasks into one model, and the second is a pure regression model. The main focus is on the non-linear activation functions used in neuronal network models and their discretization errors. The two use cases will demonstrate the quality of the proposed scaling approach and the simplicity of the VM programming for classification and regression models, including CNN architectures.