  The field of mimicking the structure of the brain on a chip is experiencing
interest driven by the demand for machine intelligent applications. However,
the power consumption and available performance of machine-learning (ML)
accelerating hardware still leave much desire for improvement. In this letter,
we share viewpoints, challenges, and prospects of electronic-photonic neural
network (NN) accelerators. Combining electronics with photonics offers
synergistic co-design strategies for high-performance AI Application-specific
integrated circuits (ASICs) and systems. Taking advantages of photonic signal
processing capabilities and combining them with electronic logic control and
data storage is an emerging prospect. However, the optical component library
leaves much to be desired and is challenged by the enormous size of photonic
devices. Within this context, we will review the emerging electro-optic
materials, functional devices, and systems packaging strategies that, when
realized, provide significant performance gains and fuel the ongoing AI
revolution, leading to a stand-alone photonics-inside AI ASIC 'black-box' for
streamlined plug-and-play board integration in future AI processors.
