Hardware Accelerator for Ethanol Detection in Water Media based on Machine Learning Techniques

In the last years, the Industry 4.0 paradigm is gaining relevance in the agro-food industry, leading to Smart Farming. One of the applications in the Smart Farming domain is the advanced chemical analysis in process monitoring using distributed, low-cost embedded systems. Optical sensing technology is used in conjunction with machine learning techniques for this advanced analysis. From the embedded system perspective, it might be required to propose a method for the implementation of machine learning techniques in heterogeneous platforms. This paper focuses on implementing Machine Learning techniques in a System on Programmable Chip, based on an FPGA and ARM processors. As a use case, we mimic water pollution by ethanol. Thus, the application might determine the percentage of ethanol of the water during run-time. As a result, this paper provides a methodology for implementing a machine learning technique for ethanol prediction using an FPGA, and the study of its parameters as resource utilization and accelerator latency for the architecture proposed