Modeling Silicon-Photonic Neural Networks under Uncertainties
Sanmitra Banerjee1, Mahdi Nikdast2 and Krishnendu Chakrabarty1
1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
2Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
ABSTRACT
Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehensive and hierarchical study on the impact of random uncertainties on the classification accuracy of a Mach–Zehnder Interferometer (MZI)- based SPNN. We show that such impact can vary based on both the location and characteristics (e.g., tuned phase angles) of a non-ideal silicon-photonic device. Simulation results show that in an SPNN with two hidden layers and 1374 tunable-thermalphase shifters, random uncertainties even in mature fabrication processes can lead to a catastrophic 70% accuracy loss.
