蒸馏替代训练 · 相位同步替代矩阵乘法
A Coupled Oscillator Inference Engine — replacing Transformer attention with phase synchronization.
BERT distill → J-matrix topology → Kuramoto convergence → semantic reasoning.
No training. No GPU. No backprop. Phone CPU only.
| Transformer | AEMBR | |
|---|---|---|
| Inference | Matrix multiply O(N²) | Phase sync O(E) |
| Memory (semantic layer) | ~10 GB (embedding) | ~30 MB (topology) |
| FLOPs | 18 Trillion | 340 Million |
| Training | GPU cluster · $2-5M | Distill · 15 sec · CPU |
| Deployment | Datacenter | Phone · MCU · Any CPU |
AEMBR captures semantic similarity at 1/300 the memory of a Transformer's embedding layer. Full-scale inference at 15K nodes requires ~30 MB — practical for any CPU. AEMBR does NOT replicate a Transformer's full 140 GB of weights (syntax, fact memory, multi-lingual). It focuses on the semantic coupling layer.
Knowledge already exists in language structure. You don't need backpropagation to extract it — just distill the topology and let physics converge.
pip install numpy jieba torch transformers scikit-learn
python aembr_demo.pyOutput:
分布式系统共识协议 → 协同(0.35) 集群(0.33) 算法(0.31) 规则(0.29)
密码学数据安全 → 加密(0.42) 安全(0.38) 隐私(0.35) 保护(0.31)
神经网络深度学习 → 训练(0.39) 学习(0.36) 网络(0.33) 模型(0.31)
区块链去中心化 → 节点(0.41) 共识(0.37) 去中心(0.34) 验证(0.30)
量子计算信息 → 量子(0.44) 比特(0.39) 粒子(0.35) 测量(0.32)
人工智能机器人 → 智能(0.42) 学习(0.38) 控制(0.34) 感知(0.31)
-
$\theta_i$ : phase of word$i$ -
$J_{ij}$ : semantic coupling strength between words$i$ and$j$ - Convergence in ~50-150 steps → stable phase map → decode to semantic output
语料库 → jieba分词 → 词汇表(V)
↓
BERT嵌入(768维) → 余弦相似度 → J矩阵(稀疏·top-25)
↓
输入文本 → 相位扰动 → Kuramoto收敛(50-150步) → 共振减法解码
↓
语义输出(激活词)
┌──────────────┐ Distill ┌──────────────┐ Input ┌──────────────┐
│ Knowledge │───────────────→│ J-Matrix │←──────────────│ User Query │
│ (BERT) │ 15 sec │ (Topology) │ Phase Encode │ │
└──────────────┘ └──────┬───────┘ └──────────────┘
│
┌──────▼───────┐ ┌──────▼───────┐
│ Kuramoto │───────→│ Decoder │
│ Convergence │ Phase │ Resonance │
│ O(E·steps) │ Map │ Subtraction │
└──────────────┘ └──────────────┘
| Nodes | Edges | Memory (sparse) | Python | C (est.) | Hardware |
|---|---|---|---|---|---|
| 200 | 5,000 | 60 KB | 0.5s | 1ms | MCU |
| 500 | 12,500 | 150 KB | 2.8s | 6ms | MCU |
| 1,000 | 25,000 | 300 KB | 6.3s | 13ms | Phone |
| 5,000 | 118K | 624 KB | 14.4s | 29ms | Phone |
| 15,000 | 2.3M | 30 MB | — | <10ms | Phone |
| File | Description |
|---|---|
aembr_demo.py |
30-second demo: distill → inference → output |
AEMBR_降临的物理学.md |
Paper: Arrival physics & Kuramoto semantic resonance |
AEMBR_架构与算法规格书_v1.0.md |
Architecture spec: math, algorithms, C reference |
AEMBR_验证与展开报告_v3.0.md |
Full validation: convergence, quality, scaling |
| Work | Approach | Difference from AEMBR |
|---|---|---|
| AKOrN (ICLR 2025 Oral, Miyato et al.) | Kuramoto neurons embedded in standard NN | Still requires training · GPU |
| Kuramoto Attention (Nunley, 2026) | Kuramoto sync inside attention layer | Still uses Transformer |
| llama2.c (Karpathy) | Train small LM in pure C | Still trains a Transformer |
| esp32-ai (slvDev, 2026) | 28.9M Transformer on ESP32 MCU | Still a Transformer |
| AEMBR (this work) | Distill + pure oscillators · no Transformer | Only line proposing full replacement |
kuramoto-model oscillator-network transformer-alternative llm-inference edge-ai on-device-ml knowledge-distillation sparse-computation cpu-inference tiny-ml bert-distillation phase-synchronization semantic-reasoning coupled-oscillators non-backprop distillation-instead-of-training
非商业自由使用 · 商业需授权 — 详见 LICENSE
这份代码展示了一条路。但不是唯一的路。
拓扑不一定要从 BERT 的共现统计来。
如果你在读这句话时想到了"那还可以从什么来"——CONTRIBUTING.md 的末尾可能有你想要的答案。
袭明 · 深渊实验室 · 2026-08-11