Intelligent Computing-in-Memory Architecture and Method Based on Dynamic Resonant Topology Processing

All existing computers and chips, whether consumer-grade CPUs/GPUs or specialized AI acceleration chips, are fundamentally built on the von Neumann architecture. This nearly 80-year-old architecture has hit three insurmountable fundamental bottlenecks in today's intelligent scenarios:Energy Efficiency Crisis Caused by the "Memory Wall"In the von Neumann architecture, computing units and data storage units are completely separated. For a single AI computation, over 80% of the time and power consumption is spent moving data from the storage unit to the computing unit and transferring the results back. Even with an extremely fast computing unit, performance is bottlenecked by the data movement process, resulting in high latency and excessive power consumption that cannot support real-time intelligent scenarios at the edge.Serial Instruction-Driven Model Incompatible with Intelligent Scenario RequirementsTraditional computing is "instruction-driven": programmers write sequential instructions, and the chip executes them step by step in order. However, scenarios such as autonomous driving and robotics need to process multimodal unstructured data from vision, touch, and audio, requiring real-time parallel processing and rapid adaptation to unknown environments. The serial execution model is inherently inefficient and inflexible for these use cases.Existing AI and Neuromorphic Solutions Fail to Capture the Core of Biological IntelligenceCurrent deep learning relies heavily on offline training with massive annotated data. Once trained, the model is fixed, prone to failure in unseen scenarios, and suffers from catastrophic forgetting – "learning new things erases old knowledge". Most existing neuromorphic chips only simulate the firing behavior of individual neurons and synapses, without replicating the core capability of the human brain: information is not stored in fixed memory addresses, but encoded, stored, and computed through dynamic resonant connection topologies between large clusters of neuron nodes. Learning is the self-evolution of the topological structure, and these chips also fail to deeply integrate intelligence with the closed-loop interaction between the body and the environment.