This proposal outlines a novel quantum-inspired AI chip architecture for implementing SGS.ai systems in hardware. The design leverages concepts from quantum mechanics (particularly entanglement and superposition) to create a stochastic, energy-efficient computing paradigm that bridges classical AI with quantum-inspired relational processing.

At its core, the architecture consists of:

• A static-dynamic brain structure combining HyperLogLog probabilistic sets (HLLSets) as neurons with von Neumann automata for self-generation
• Perceptron interfaces that mediate between environmental sensors/actuators and the core brain structure
• Quantum-inspired properties including entanglement-like correlations between data representations and superposition-like state management

By combining probabilistic data structures with quantum-inspired principles, it achieves:

• Hardware-efficient relational reasoning
• Explainable cross-modal learning
• Energy-efficient operation

Native path to quantum enhancement