Patent classifications
H04Q2213/054
SILICON PHOTONICS-BASED CHIPLET ACCELERATOR FOR DNN INFERENCE
A computer architecture has a global optical waveguide, a buffer having memory space having a memory hierarchy above main memory for temporary data storage. A transmitter transmits a first optical signal with a first plurality of optical wavelengths on the global optical waveguide, and a second optical signal with a second plurality of optical wavelengths on the global optical waveguide. A receiver receives a second optical signal with a third plurality of optical wavelengths from the global optical waveguide. One or more local optical waveguide(s) are coupled to the global optical waveguide to receive all of the first plurality of optical wavelengths and a unique wavelength of the second plurality of optical wavelengths and transmit a unique wavelength of the third plurality of optical wavelengths. A plurality of chiplets are coupled to one of one or more local optical waveguides, each of the plurality of chiplets have a plurality of processing elements each receiving one of the first plurality of optical wavelengths and one of the second plurality of optical wavelengths from the local optical waveguide and transmitting one of the third plurality of optical wavelengths to local optical waveguide.
FAULTY SEGMENT ESTIMATION METHOD, FAULTY SEGMENT ESTIMATION SYSTEM, AND FAULTY SEGMENT ESTIMATION APPARATUS
Provided is an abnormal section estimation method in a system in which an optical transmitter and an optical receiver are connected by an optical transmission line, the optical transmission line being divided into a plurality of sections at one or more monitoring points from the optical transmitter to the optical receiver, the abnormal section estimation method including: extracting, based on an optical signal transmitted from the optical transmitter, signal data on a complex plane of the optical signal expressed by phase and amplitude at the one or more monitoring points; acquiring an abnormality estimation result of at least one of the plurality of sections by inputting the signal data extracted at the one or more monitoring points to trained models trained to receive the signal data as input and output the abnormality estimation result; and estimating a section where an abnormality has occurred based on the acquired abnormality estimation result.