Open White Paper on Geophysical Computing: Ubiquitous Decentralized Computing Bus and Multi-Track Decoupled Dynamo Architecture (Project Amanda, Version 2.2)

Abstract

This white paper presents Project Amanda (Version 2.2), a novel decentralized neuromorphic hardware architecture designed to break through the constraints of conventional deterministic computing paradigms. Conventional neuromorphic computing relies excessively on cost-prohibitive pre-hardware perceptual training and cumbersome hardware defect calibration algorithms, leading to unsustainably high computing costs and energy footprints at the edge. Breaking away from traditional deterministic paradigms, this framework introduces a dynamic topological computing generation model founded on robust control constraints, which utilizes retired or low-yield non-volatile memory elements, harmonizing with their intrinsic physical anisotropy and electrical aging patterns. By implementing an atomic parameter fragmentation approach, this architecture partitions large language models to run directly on legacy or high-defect-rate storage elements, such as RRAM or FeRAM particles, without requiring any expensive pre-hardware noise simulation training. The hardware layer incorporates a Multi-Track Asynchronous Dynamo topology paired with ultra-fast recovery anti-backflow Schottky diodes. By treating localized filament fractures and resistance drift as structured perturbations under the H-infinity robust control framework, the architecture restricts the energy-to-energy gain of localized voltage fluctuations within an H-infinity norm bound of less than one, transforming potential catastrophic thermal breakdown into a self-limiting thermodynamic avalanche. For fluidic computing stream convergence, the fluidic escape routing algorithm is mathematically verified via the Input-to-State Stability (ISS) criterion, guaranteeing that continuous spontaneous branch tripping asymptotically converges without inducing chaotic internal voltage divergence. Furthermore, through an asynchronous closed-loop conveyor pipeline and multi-track carbon fiber pulse-checking brushes, the architecture achieves autonomous gravitational metabolism and periodic thermal annealing. To overcome conductance drift over long durations, the architecture introduces a Dynamic Heterogeneous Co-computing model where the zero-error 5 percent core parameters stored on high-performance fresh hardware act as an absolute robust state observer. This observer executes real-time inverse error attenuation to suppress parameter fluctuations, extracting an organic 1 percent to 5 percent Carbon-Specific Bias that prevents model collapse and functions as a cognitive blindspot to shield the network from external silicon-based reverse engineering. Seamlessly embedded within light-source parasitic urban infrastructures and LEO satellite constellations, Project Amanda constructs a ubiquitous computing background radiation network across objective physical space, democratizing advanced intelligence for all humanity at an exceptionally low marginal energy cost.