Topological Compression: Bypassing the High-Bandwidth Memory (HBM) Bottleneck via Teixido-Boreal Sparsity

The scaling of Large Language Models (LLMs) is currently throttled by the "Memory Wall"—specifically, the scarcity, power cost, and latency of High-Bandwidth Memory (HBM). We propose a Structural Sparsity paradigm based on the Teixido-Boreal Forest architecture. Grounded in the machine-verified Teixido Envelope (τ=−0.5), we introduce a method for Topological Compression. Key Benchmarks (7B Parameter Scale): Extreme Model Compression: By locking connectivity to a Degree-15 Random Regular skeleton, we achieve a 97.1% reduction in synaptic parameter storage. Simulations demonstrate a reduction from 13.04 GB (Standard FP16) to 0.57 GB (Teixido-Boreal). SRAM Residency: The 52x reduction in logic gate area allows chip architects to reallocate silicon for on-chip memory, enabling LLMs to run entirely within SRAM buffers, eliminating off-chip DRAM latency. Throughput Velocity: RTL verification of the memory controller confirms a 16x reduction in bus contention cycles. On bandwidth-constrained edge hardware, this results in a projected 23.0x increase in tokens-per-second throughput. Processing-In-Memory (PIM): The multiplication-free Tropical logic is suitable for embedding directly into commodity memory modules. This report validates a path to Memory Independence, effectively decoupling AI performance from the HBM supply chain constraints. Commercial Licensing:Proprietary memory controller RTL, optimized sparsity masks, and commercial licenses for the Teixido-ISA are available for semiconductor manufacturers.Contact: jvteixido@liberty.edu