Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks.
Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics.
