Flexible Error Modeling and Analysis of SiPho JTC Accelerators for CNNs

This paper describes a system for modeling nonidealities in a silicon-photonic joint transform correlator (JTC) used as an accelerator for convolutional neural networks (CNNs). The system is used to determine which nonidealities have the greatest impact on end-to-end model accuracy, which helps determine what design choices can be made at the block and system level to improve performance. We create a digital twin of the on-chip 1D JTC using transfer functions fitted from simulations, then introduce a scalar α that mixes ideal and realized behaviors. We quantify the α -dependent model accuracy on CIFAR-10 and SNDR differences at both the joint-power spectrum (JPS) and the JTC output. The framework allows quantification of block- and system-level sensitivities, informing design choices by isolating the accuracy bottlenecks within the system.