It can be challenging to engage with machine learning in restricted computing environments, such as the systems often in use in digital or hybrid musical instruments. We often use low power, low memory devices with limited computational power, and need high frequency models for sensor and sound processing. Conversely, contemporary machine learning and AI can be resource hungry, limiting its use in embedded systems. In previous research, the Stochastic Logic Optimisation algorithm offered a method of lightweight machine learning using two-state logic networks, intended for musical use in embedded systems. This experiment shows how this approach can be expanded on, using random boolean reservoirs, for signal generation. These initial results demonstrate the efficacy of a reservoir computing approach, built only from networks of lookup tables. They show that, for the task of training sine wave generators, reservoirs can be improved if built with hierarchical growth algorithms, and further improved by selecting inputs and outputs based on network centrality. The results also demonstrate successful use of Pulse Density Modulation for signal encoding.