In recent decades, the demand for computational power has surged, particularly with the
rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the …

Photonic neural networks (PNNs) are remarkable analogue artificial intelligence
accelerators that compute using photons instead of electrons at low latency, high energy …

Physical neural networks (PNNs) are a class of neural-like networks that leverage the
properties of physical systems to perform computation. While PNNs are so far a niche …

In the post Moore's era, conventional electronic digital computing platforms have
encountered escalating challenges to support massively parallel and energy-hungry …

Emerging neuromorphic hardware promises to solve certain problems faster and with higher
energy efficiency than traditional computing by using physical processes that take place at …

Optical computing has become emerging technology in next-generation efficient artificial
intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is …

Optical neural networks (ONNs) are promising hardware platforms for next-generation
artificial intelligence acceleration with ultrafast speed and low-energy consumption …

Photonic computing has emerged as a promising solution for accelerating computation-
intensive artificial intelligence (AI) workloads. However, limited reconfigurability, high …

Optical neural networks (ONNs) are promising hardware platforms for next-generation
neuromorphic computing due to their high parallelism, low latency, and low energy …

Backward propagation (BP) is widely used to compute the gradients in neural network
training. However, it is hard to implement BP on edge devices due to the lack of hardware …