Conventional computing based on digital electronic logic faces challenges due to the rapid growth of artificial intelligence and machine learning algorithms, which need massive amounts of processing power, outpacing the rate at which computers have historically progressed (according to Moore's Law). Photonics technologies offer an attractive alternative due to several advantages, including high speed, energy efficiency, and the potential for massive parallelization of information processing. This project aims to tackle the obstacles in using integrated photonic deep neural networks for the next generation of computing platforms. Despite previous efforts, these networks still face challenges that make them impractical for real-world applications. In particular, most previous photonic neural networks are not scalable and require electronic circuits for achieving nonlinear effects, thus inevitably losing the high-speed advantages of photonics. An interdisciplinary approach is needed to tackle several problems, including designing novel neural network architectures for efficient processing of information encoded in light and integrating different materials for achieving desired functionalities, such as reconfigurability and nonlinear effects, which are essential for machine learning. This project focuses on developing a new deep learning architecture that is compatible with photonics and optoelectronics technologies and significantly reduces the size of optical neural networks c