  The computational demands of modern AI have spurred interest in optical
neural networks (ONNs) which offer the potential benefits of increased speed
and lower power consumption. However, current ONNs face various challenges,most
significantly a limited calculation precision (typically around 4 bits) and the
requirement for high-resolution signal format converters (digital-to-analogue
conversions (DACs) and analogue-to-digital conversions (ADCs)). These
challenges are inherent to their analog computing nature and pose significant
obstacles in practical implementation. Here, we propose a digital-analog hybrid
optical computing architecture for ONNs, which utilizes digital optical inputs
in the form of binary words. By introducing the logic levels and decisions
based on thresholding, the calculation precision can be significantly enhanced.
The DACs for input data can be removed and the resolution of the ADCs can be
greatly reduced. This can increase the operating speed at a high calculation
precision and facilitate the compatibility with microelectronics. To validate
our approach, we have fabricated a proof-of-concept photonic chip and built up
a hybrid optical processor (HOP) system for neural network applications. We
have demonstrated an unprecedented 16-bit calculation precision for
high-definition image processing, with a pixel error rate (PER) as low as
$1.8\times10^{-3}$ at an signal-to-noise ratio (SNR) of 18.2 dB. We have also
implemented a convolutional neural network for handwritten digit recognition
that shows the same accuracy as the one achieved by a desktop computer. The
concept of the digital-analog hybrid optical computing architecture offers a
methodology that could potentially be applied to various ONN implementations
and may intrigue new research into efficient and accurate domain-specific
optical computing architectures for neural networks.
