Optical coherent dot-product chip for sophisticated deep learning regression

Kavli Affiliate: Jing Wang

| First 5 Authors: Shaofu Xu, Jing Wang, Haowen Shu, Zhike Zhang, Sicheng Yi

| Summary:

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.
However, due to the problems of incomplete numerical domain, limited hardware
scale, or inadequate numerical accuracy, the majority of existing ONNs were
studied for basic classification 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. Here, we demonstrate a
silicon-based optical coherent dot-product chip (OCDC) capable of completing
deep learning regression tasks. The OCDC adopts optical fields to carry out
operations in complete real-value domain instead of in only positive domain.
Via reusing, a single chip conducts matrix multiplications and convolutions in
neural networks of any complexity. Also, hardware deviations are compensated
via in-situ backpropagation control provided the simplicity of chip
architecture. Therefore, the OCDC meets the requirements for sophisticated
regression tasks and we successfully demonstrate a representative neural
network, the AUTOMAP (a cutting-edge neural network model for image
reconstruction). The quality of reconstructed images by the OCDC and a 32-bit
digital computer is comparable. To the best of our knowledge, there is no
precedent of performing such state-of-the-art regression tasks on ONN chip. It
is anticipated that the OCDC can promote novel accomplishment of ONNs in modern
AI applications including autonomous driving, natural language processing, and
scientific study.

| Search Query: ArXiv Query: search_query=au:”Jing Wang”&id_list=&start=0&max_results=10

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