Current silicon-based CMOS architectures face significant scalability challenges due to energy efficiency limits, thermal constraints, and memory-processor data bus bottlenecks in the face of increasing computational density. Specifically, in the Von Neumann architecture, the continuous data transfer between memory and processing units has become the dominant component of total energy consumption and latency. This paper proposes a hybrid computing architecture inspired by the in-memory computing and high energy efficiency principles of biological neural systems, yet relying entirely on solid-state and synthetic materials: Synthetic Nano-Ionic Resonance Architecture (SNIRA). SNIRA integrates metal-oxide memristors capable of analog weight storage and computation, carbon nanotube-based field-effect transistors (CNTFETs) to limit stochastic signal behavior and provide deterministic control, and integrated microfluidic cooling networks to enable stable operation at high power densities. The architecture proposes a frequency-selective and content-addressable data activation mechanism instead of classic address-based memory access. This work aims to provide a conceptual and architectural framework for scalable neuromorphic and high-performance AI systems rather than experimental validation.
Deniz (Thu,) studied this question.