  Parallel processing technology has been a primary tool for achieving
high-speed, high-accuracy, and broadband processing for many years across
modern information systems and data processing such as optical and radar,
synthetic aperture radar imaging, digital beam forming, and digital filtering
systems. However, hardware deviations in a parallel processing system (PPS)
severely degrade system performance and pose an urgent challenge. We propose a
hardware-irrelevant PPS of which the performance is unaffected by hardware
deviations. In this system, an embedded convolutional recurrent autoencoder
(CRAE), which learns inherent system patterns as well as acquires and removes
adverse effects brought by hardware deviations, is adopted. We implement a
hardware-irrelevant PPS into a parallel photonic sampling system to accomplish
a high-performance analog-to-digital conversion for microwave signals with high
frequency and broad bandwidth. Under one system state, a category of signals
with two different mismatch degrees is utilized to train the CRAE, which can
then compensate for mismatches in various categories of signals with multiple
mismatch degrees under random system states. Our approach is extensively
applicable to achieving hardware-irrelevant PPSs which are either discrete or
integrated in photonic, electric, and other fields.
