Breaking Landauer's Limit in Neuromorphic Architectures via Seonggil Matrix Theory: Utilizing Irreversible Thermodynamic Noise as a Computational Resource and Numerical Validation

The fundamental energy dissipation in conventional von Neumann architectures is bounded by Landauer’s limit, E ≥ k_BT ln2 per bit erasure. In this paper, we propose a paradigm shifting neuromorphic architecture that circumvents this thermodynamic barrier. By directly mapping the Seonggil Decryption Algorithmic Engine onto a continuous-time dynamical system, we utilize irreversible thermodynamic noise not as a source of decoherence, but as an active computational resource. Embedded within an UltraScale+ FPGA framework, the application of Seonggil Matrix Theory reduces the energy consumption of massive AI tensoroperations from the megawatt scale down to sub-20W, realizing a true ultra-low-power edge neuromorphic system. Furthermore, we provide a complete numerical simulation framework validating the sub-threshold energy dissipation and continuous-time convergence.