[2604.02429] Photonic convolutional neural network with pre-trained in-situ training

Computer Science > Emerging Technologies arXiv:2604.02429 (cs) [Submitted on 2 Apr 2026] Title:Photonic convolutional neural network with pre-trained in-situ training Authors:Saurabh Ranjan, Sonika Thakral, Amit Sehgal View a PDF of the paper titled Photonic convolutional neural network with pre-trained in-situ training, by Saurabh Ranjan and 2 other authors View PDF HTML (experimental) Abstract:Photonic computing is a computing paradigm which have great potential to overcome the energy bottlenecks of electronic von Neumann architecture. Throughput and power consumption are fundamental limitations of Complementary-metal-oxide-semiconductor (CMOS) chips, therefore convolutional neural network (CNN) is revolutionising machine learning, computer vision and other image based applications. In this work, we propose and validate a fully photonic convolutional neural network (PCNN) that performs MNIST image classification entirely in the optical domain, achieving 94 percent test accuracy. Unlike existing architectures that rely on frequent in-between conversions from optical to electrical and back to optical (O/E/O), our system maintains coherent processing utilizing Mach-Zehnder interferometer (MZI) meshes, wavelength-division multiplexed (WDM) pooling, and microring resonator-based nonlinearities. The max pooling unit is fully implemented on silicon photonics, which does not require opto-electrical or electrical conversions. To overcome the challenges of training physical phase ...