Gaussian Process Regression Based Robust Optimization with Observer Uncertainty for Reconfigurable Self-x Sensory Electronics for Industry 4.0

Abstract

This paper presents a robust optimization technique for the reconfigurable measurement of sensory electronics for industry 4.0 to obtain a robust solution even in the presence of observer uncertainty using a cost-effective performance measurement method. The extrinsic evaluation of the proposed methodology is performed on an indirect current-feedback instrumentation amplifier (CFIA), which is a fundamental part of sensory systems. To reduce the CFIA device performance evaluation set-up cost, a low-cost test stimulus is applied to the circuit under test, and the output response of the circuit is examined to correlate with the device’s performance parameters. Due to the complexity of the smart sensory electronics search space, the meta-heuristic optimization algorithm is being selected as an optimizer. For objective space or observer uncertainty, the Gaussian process regression from the Bayesian statistical regression process is used to estimate the uncertainty level efficiently. Six different classical metrics have been used to evaluate the regression model accuracy. The highest achieved average expected error metrics value is 0.313, and the minimum value of correlation performance metrics is 0.908. The device is implemented using 0.35 μm austriamicrosystems technology.

Integrated Sensor Electronic Front-Ends with Self-X Capabilities

“…The GPR is optimized by adapting the kernel and mean value according to the training data set. Figure 22 compares the distorted output signal of the CFIA and the predicted output signal of the robust optimizer using GPR with 95% confidence intervals [110]. As observed from the figure, in addition to uncertainty level prediction, the GPR helps forecast the data, which can considerably minimize the transmission power of wireless sensor network applications.…”

“…In the light of the design issues that unfortunately occurred in the USIX 2, the focus was shifted from providing a complete USIX 3 chip to elaborating in more detail solutions and advance for crucial cells and components in XFAB xh035 µm CMOS technology, validate those by a more modest design, as detailed in Sections 3 and 5, and in a following step, use the validated key cells to compile a complete USIX 3 chip. The main important issues relate to determining the minimum number and optimum location of tuning knobs, improvement of optimization techniques in the self-X loops, and last but not least, tackling imperfections and obtrusiveness in the optimizer/observer [109][110][111][112][113][114][115][116][117][118][119] in the named self-X loops.…”

“…To address the observer imperfections, we explored the concept of robust optimization in [109,110]. The robust optimization can be split into two categories [116,185].…”

“…The second category is based on the surrogate-based robust optimization method [185]. The famous Bayesian statistical regression process, more precisely, the Gaussian process regression (GPR) [110,117,118], falls under this umbrella. The GPR has been commonly used for error quantification and design optimization.…”