Optimization of the fixed-point representation of measurement data for intelligent measurement systems

Abstract

The development of intelligent measurement systems by implementing AI (artificial intelligence) algorithms on edge sensor nodes is very topical. Due to very limited hardware of sensor nodes, the choice of the optimal digital format for representation of measurement data and parameters of AI algorithms becomes an important issue. The floating-point format is impractical to implement on sensor nodes due to great complexity, while the fixed-point format has significantly less complexity, representing an ideal solution for intelligent sensor nodes. This paper is dealing with performance analysis and optimization of the fixed-point format for measurement data modeled by the Gaussian distribution. The performance analysis is performed in a generalized way, having wide applicability. Furthermore, the paper proposes a rule for optimization of the fixed-point format, providing a high and almost constant level of quality of the fixed-point representation in a very wide variance range of measurement data. Results are confirmed by simulations.