Photonic in Memory Neurocomputing

Photonic in-memory neurocomputing aims to accelerate neural network computations by performing calculations directly within an optical memory, thereby reducing the energy-intensive data movement inherent in traditional architectures. Current research focuses on optimizing convolutional neural networks using novel photonic memory devices, such as memresonators integrated with silicon photonics, to achieve high speed and low energy consumption. This approach shows promise for significantly improving the efficiency and scalability of machine learning, particularly by reducing write operations and extending the lifetime of the optical memory components.