Explainable neural networks may be designed to be easily implementable in hardware efficiently, leading to substantial speed and space improvements. An exemplary embodiment extends upon possible hardware embodiments of XNNs, making them suitable for low power applications, smartphones, mobile computing devices, autonomous machines, server accelerators, Internet of Things (IoT) and edge computing applications amongst many other applications. The capability of XNNs to be transformed from one form to another while preserving their logical equivalence is exploited to create efficient, secure hardware implementations that are optimized for the desired application domain and predictable in their behavior.