Efficient transient testing procedure using a novel experience replay particle swarm optimizer for THD-based robust design and optimization of self-X sensory electronics in industry 4.0

Abstract

Abstract. This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.

Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

“…To address this issue, enhance long-term reliability, and improve the CFIA power efficiency, we incorporated a lowcost indirect PMM with a THD-based optimization methodology. Finally, we chose our proposed experience replay particle swarm optimization (ERPSO) [37], which is a modified version of PSO, as the optimization unit.…”

“…To minimize the impact of random occurrences or lucky shots, the optimization process was performed for 10 independent runs on the chip marked as number 1. Comprehensive information on the optimization algorithm can be found in [37,63]. The mean value of the error-convergence curve for the optimization algorithm is illustrated in Figure 21.…”

“…This work involves placing the AI agent at the hardware level closest to the device under test (DUT). The proposed experience replay particle swarm optimization (ERPSO) [37] has been selected as the choice of AI-based optimizer and implemented on the field programmable gate array (FPGA) board provided by the Red Pitaya [38], which functions as an edge computing device. As far as long-term chip reliability and power efficiency are con-cerned, an indirect power monitoring module (PMM) is integrated with the THD-based optimization methodology.…”

“…The ERPSO algorithm enhances the classical PSO algorithm's selection producer by randomly selecting the historical global best of particles using the experience replay buffer (ERB) to tackle the complex objective space problem in SSEs [37]. The ERB concept, commonly used in reinforcement learning [65,66], leverages the accumulated historical values to improve convergence accuracy.…”

Design of a CMOS memristor emulator-based, self-adaptive spiking analog-to-digital data conversion as the lowest level of a self-x hierarchy

“…The performance of the intelligent sensor faces a lot of issues due to static and dynamic variations. These issues are effectively addressed by using reconfigurable structures with the self-X (self-calibration, self-optimization, and self-healing) properties (Alraho et al, 2020;Zaman et al, 2021;Aamir et al, 2018;Jo et al, 2019;Lee et al, 2018b;Lu et al, 2013;Koenig, 2018). However, reconfigurable structures are still utilizing amplitude-coded signals that face issues with downscaling technologies.…”