  Photonics offers a transformative approach to artificial intelligence (AI)
and neuromorphic computing by providing low latency, high bandwidth, and
energy-efficient computations. Here, we introduce a photonic tensor core
processor enabled by time-multiplexed inputs and charge-integrated outputs.
This fully integrated processor, comprising only two thin-film lithium niobate
(TFLN) modulators, a III-V laser, and a charge-integration photoreceiver, can
implement an entire layer of a neural network. It can execute 65 billion
operations per second (GOPS) per neuron, including simultaneous weight
updates-a hitherto unachieved speed. Our processor stands out from conventional
photonic processors, which have static weights set during training, as it
supports fast "hardware-in-the-loop" training, and can dynamically adjust the
inputs (fan-in) and outputs (fan-out) within a layer, thereby enhancing its
versatility. Our processor can perform large-scale dot-product operations with
vector dimensions up to 131,072. Furthermore, it successfully classifies
(supervised learning) and clusters (unsupervised learning) 112*112-pixel images
after "hardware-in-the-loop" training. To handle "hardware-in-the-loop"
training for clustering AI tasks, we provide a solution for multiplications
involving two negative numbers based on our processor.
