  In the dynamic landscape of Artificial Intelligence (AI), two notable
phenomena are becoming predominant: the exponential growth of large AI model
sizes and the explosion of massive amount of data. Meanwhile, scientific
research such as quantum computing and protein synthesis increasingly demand
higher computing capacities. Neuromorphic computing, inspired by the mechanism
and functionality of human brains, uses physical artificial neurons to do
computations and is drawing widespread attention. Conventional electronic
computing has experienced certain difficulties, particularly concerning the
latency, crosstalk, and energy consumption of digital processors. As the
Moore's law approaches its terminus, there is a urgent need for alternative
computing architectures that can satisfy this growing computing demand and
break through the von Neumann model. Recently, the expansion of optoelectronic
devices on photonic integration platforms has led to significant growth in
photonic computing, where photonic integrated circuits (PICs) have enabled
ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low
heat dissipation, and high parallelism. Such non-von Neumann photonic computing
systems hold the promise to cater to the escalating requirements of AI and
scientific computing. In this review, we study recent advancements in
integrated photonic neuromorphic systems, and from the perspective of materials
and device engineering, we lay out the scientific and technological
breakthroughs necessary to advance the state-of-the-art. In particular, we
examine various technologies and devices employed in neuromorphic photonic AI
accelerators, spanning from traditional optics to PICs. We evaluate the
performances of different designs by energy efficiency in operations per joule
(OP/J) and compute density in operations per squared millimeter per ...
