Implementing large-scale linear transformations or matrix computations plays a pivotal role in modern information processing systems. Digital computer systems need to complete up to billions of matrix operations per second to perform complex computational tasks, such as training and inference for deep neural networks. As a result, the throughput of linear transform computations can directly influence the performance and capacity of the underlying computing systems. These linear transformations are computed using digital processors in computers, which can face bottlenecks as the size of the data to be processed gets larger and larger. This is where all-optical computing methods can potentially provide a remedy through their parallelism and speed.
Click here for original story, All-optical computation of a group of transformations using a polarization-encoded diffractive network
Source: Phys.org