Markovian graphs with cycles are difficult to solve, especially in remote or military areas with limited power. A programmable optical network can solve these computations faster and with less weight. Factor graphs break down complex problems like SLAM into trees, but undirected graphs with cycles are the most challenging to solve. They have links to quantum computing and logistical problems like the traveling salesman problem. Neoskye proposes The Optical Programmable Computing Network Architecture Supporting Iterative Multicast for Data-intensive Applications such as solving Markovian graphs with cycles and Forney-style factor graphs while avoiding the creation of trees. Neoskye's proposed architecture supporting iterative multicast will significantly improve the performance, scalability, and efficiency of solving Markovian graphs with cycles and Forney-style factor graphs while avoiding the creation of trees. This will lead to faster and more accurate solutions. Our programmable optical system design will support iterative multicast traffic at a fine timescale, leveraging wavelength conversion to support multiple multicast groups, wavelength selective switches (WSS) to achieve fast reconfiguration, and Software Defined Networking (SDN) to intelligently place WSSs and splitters to form efficient and flexible data distribution trees and more efficiently solve Markovian graphs with cycles and Forney-style factor graphs while avoiding the creation of trees. We are determined to achieve a convergence rate of no less than 95% and a solution accuracy of no less than 99%, all while avoiding unnecessary tree creation. We are also committed to keeping the number of iterations to less than 10. we are determined to reduce the Optical computing Network's power usage by 40%.
