Self-aware Hierarchical Neuromorphic Architecture

We introduce a neuromorphic architecture devised by mapping biologically inspired concepts onto a computational framework for self-awareness, offering a new way to embed inherent autonomy directly within hardware. Leveraging this architecture as a blueprint, we have designed a memristor-based circuit capable of autonomous decision-making, employing a bio-inspired hierarchical design approach. The core of this approach involves translating the functions of biological neurons and synapses into memristor-based circuits, which act as efficient computational units at the bottom layer of the circuit. It ensures that the developed circuit strikes a balance between functional flexibility and computational efficiency. Experimental results show that our implemented circuit provides efficient computational capabilities for effective decision-making, thanks to its memristor-based bio-inspired hierarchical implementation. Furthermore, its neuromorphic architecture inspired by self-awareness facilitates the easy simulation of reliable autonomous behaviors. Through the analysis of hardware overhead and power consumption, our circuit proves to be hardware-friendly. Our work represents progress towards developing memristor-based neuromorphic circuits characterized by high computational performance and autonomous functionality.