Abstract
State-of-the-art Large Language Models (LLMs) and neuromorphic hardware suffer from massive static power consumption due to continuous memory-refresh cycles and leakage currents, we introduce the Cryptobiotic Neuromorphic Architecture (CNA). CNA bypasses traditional von Neumann and non-volatile memory (NVM) paradigms by utilizing a novel Vitrifactor-driven Chalcogenide Matrix (VCM).
When structural components of a neural network (sub-graphs, weights, or attention heads) become inactive, the system triggers a localized, simulated chemical transition called Vitrification. This transition locks active weights into a zero-power, non-volatile "glassy" state (Dormant Memory), structurally immune to cosmic radiation, thermal fluctuations, and total power deprivation. Upon activation signals, the state is dynamically hydrated (sub-nanosecond phase-field restoration) back into an active computational regime. This paper details the hardware physics, the algorithmic compiler, and the fault-tolerant mechanisms governing this truly zero-leakage, ultra-resilient AI paradigm.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny and Johnson, Jayden Jack, "CRYPTOBIOTIC NEUROMORPHIC ARCHITECTURE (CNA): VOLUMETRIC GLASS-PHASE MEMORY HIBERNATION FOR ZERO-POWER FAULT-TOLERANT AI", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10902