Artificial intelligence (AI) has become a leading field in many scientific and technical research and industrial activities. This trend is supported by increasingly complex AI models (e.g., deep neural networks, transformers, LLMs, etc.) that require large-scale computing infrastructures. The ever-increasing energy consumption of these infrastructures is driving research towards more efficient AI systems. In this sense, research aims to develop AI models whose execution (inference) does not require complex mathematical operations (e.g., matrix-matrix or vector-matrix floating-point multiplications), allowing for the use of simpler and more energy-efficient computing systems. Recently, Logic Gate Networks (LGNs) have emerged as an energy-efficient alternative to traditional models (DNNs, CNNs, etc.).