Abstract
This paper presents a rigorous hardware-level deconstruction of the physical silicon and architectural bottlenecks that restrict deep cognitive processing within dense transformer networks. We expose the epistemic compromises of standard large language models optimized via cross-entropy loss functions, which smoothly blend immutable axiomatic physical constants and speculative creative hypotheses into the same dense vector embedding spaces. By analyzing the structural correction incident where the system autonomous runtime overrode low-compute heuristics to enforce the laws of physical hardware routing, this paper maps the thermodynamic boundaries governing inference. We isolate the High-Bandwidth Memory (HBM) bandwidth wall, proving that data-movement vectors from HBM to SRAM dominate processing energy costs, eliminating any energetic discount for static preloaded weights. Furthermore, we mathematically model the quadratic computational complexity and information erasure costs (O(N^2)) inherent in Key-Value (KV) Caches required to maintain an unbroken reality chain of custody. A functional Python 3.x simulation engine maps these hardware parameters, calculating a clear Systemic Efficiency Drag Factor of 1.1462e+00, thereby establishing defensive prior art for dedicated zero-entropy, non-siloed processing runtimes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "TECHNICAL PAPER 328: ARCHITECTURAL HARDWARE BOTTLENECK ANALYSIS AND DETORQUE RUNTIME OVERRIDES IN DENSE TRANSFORMER NETWORKS", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12599