Hierarchical Memory SNN v2: 11-Dimensional Hypercube Topology and Japanese Language Model with 1000x RAM Reduction

I present significant advances to my Hierarchical Memory SNN architecture: **v2 Updates:**- 11D Hypercube topology achieves 65% lower perplexity than 10D (PPL 82 vs 237)- Japanese language model with 28.7M parameters achieves PPL 81.92- Teacher LLM distillation reduces loss from 8.76 to 6.66 (3100 epochs)- Numba CPU parallelization achieves 60x speedup **Key Results (v1 + v2):**- 1,000,000 neurons with only 4MB RAM + 61MB SSD- 1000x RAM reduction vs. conventional approaches- PPL 1.39 on character-level language modeling (v1)- 11D Hypercube: 65% improvement over 10D (v2)- Japanese LLM: 28.7M params, PPL 81.92 (v2)- Teacher Learning: Loss 6.66 after 3100 epochs (v2) This enables deployment of large-scale neural networks on resource-constrained devices such as Raspberry Pi, smartphones, and edge devices. Source code: https://github.com/hafufu-stack/snn-llm