ADM: Efficient Training for Geometric & Neuromorphic AI

🔑 Enhanced Key Takeaways

• •The b-posit 2026 standard utilized by ADM features a 'bounded-regime' field (≤ 6 bits) that reduces decoder power consumption by 79% and silicon area by 71% compared to standard posit decoders, enabling training on edge NPU-class hardware.
• •ADM leverages Program Hypergraph (PHG) 'grade inference' to identify that approximately 95% of Cayley table entries in 3D Projective Geometric Algebra are structurally zero, resulting in a 20x code generation improvement over traditional Clifford neural networks.
• •The 'Warm Rotation' deployment pattern uses SMT-LIB2 proof infrastructure to discharge formal certificates of dimensional consistency and grade correctness, allowing models to swap in-place during active inference with zero service interruption.
• •By integrating a Deterministic Memory Management (DMM) framework, ADM replaces heap-based gradient tapes with stack-eligible gradient allocation, making training memory requirements deterministic and verifiable at design time.
• •Bayesian distillation in ADM extracts latent priors from general-purpose LLMs to initialize domain-specific 'Olivier' actors, which operate as scoped computation units overseen by a 'Prospero' supervisor actor.

🛠️ Technical Deep Dive

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline