Multi-Path Routing for Conditional Information Gain Trellis Using Cross-Entropy Search and Reinforcement Learning

Convolutional neural networks have made significant strides in solving computer-vision tasks at the expense of high computational demands. This complexity hinders efficient processing, particularly on devices with limited computational resources such as edge devices. One way to overcome this limitation is conditional computing, which optimizes inference by selectively utilizing parts of the network depending on the characteristics of the input. A recent conditional execution method is Conditional Information Gain Trellis (CIGT), which routes samples based on an information gain-based router mechanism. The original CIGT model was designed to route a single sample along a single path in a trellis structure. In this study, advanced inference strategies that allow inputs to traverse multiple paths are proposed to improve the performance of the vanilla CIGT model. These strategies aim to find a middle ground between improved model performance and increased computational demands. For this purpose, two techniques were proposed: A Cross-Entropy Search-based threshold optimization algorithm and a Reinforcement Learning-based routing strategy. The first method treats multi-path routing in CIGT as a black-box optimization problem and the second interprets it as a Markov Decision Process with a Q-Learning-based supervised regression algorithm designed as the solution. It has been shown that both of these methods provide significant performance improvements compared to the original CIGT model, with an adjustable increase in the computation. Experiments were conducted on two image datasets, with additional statistical tests and analyses to inspect the behaviors of the proposed algorithms. The novel methods designed in this study for multi-path routing show potential for both the original CIGT model and similar conditional computation approaches that use specific routing mechanisms for selecting network parts based on samples.