Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
396
vendor/ruvector/examples/exo-ai-2025/research/TECHNOLOGY_HORIZONS.md
vendored
Normal file
396
vendor/ruvector/examples/exo-ai-2025/research/TECHNOLOGY_HORIZONS.md
vendored
Normal file
@@ -0,0 +1,396 @@
|
||||
# Technology Horizons: 2035-2060
|
||||
|
||||
## Future Computing Paradigm Analysis
|
||||
|
||||
This document synthesizes research on technological trajectories relevant to cognitive substrates.
|
||||
|
||||
---
|
||||
|
||||
## 1. Compute-Memory Unification (2035-2040)
|
||||
|
||||
### The Von Neumann Bottleneck Dissolution
|
||||
|
||||
The separation of processing and memory—the defining characteristic of conventional computers—becomes the primary limitation for cognitive workloads.
|
||||
|
||||
**Current State (2025)**:
|
||||
- Memory bandwidth: ~900 GB/s (HBM3)
|
||||
- Energy: ~10 pJ per byte moved
|
||||
- Latency: ~100 ns to access DRAM
|
||||
|
||||
**Projected (2035)**:
|
||||
- In-memory compute: 0 bytes moved for local operations
|
||||
- Energy: <1 pJ per operation
|
||||
- Latency: ~1 ns for in-memory operations
|
||||
|
||||
### Processing-in-Memory Technologies
|
||||
|
||||
| Technology | Maturity | Characteristics |
|
||||
|------------|----------|-----------------|
|
||||
| **UPMEM DPUs** | Commercial (2024) | First production PIM, 23x GPU for memory-bound |
|
||||
| **ReRAM Crossbars** | Research | Analog VMM, 31.2 TFLOPS/W demonstrated |
|
||||
| **SRAM-PIM** | Research | DB-PIM with sparsity optimization |
|
||||
| **MRAM-PIM** | Research | Non-volatile, radiation-hard |
|
||||
|
||||
### Implications for Vector Databases
|
||||
|
||||
```
|
||||
Today: 2035:
|
||||
┌─────────┐ ┌─────────┐ ┌─────────────────────────────┐
|
||||
│ CPU │◄─┤ Memory │ │ Memory = Processor │
|
||||
└─────────┘ └─────────┘ │ ┌─────┐ ┌─────┐ ┌─────┐ │
|
||||
▲ ▲ │ │Vec A│ │Vec B│ │Vec C│ │
|
||||
│ Transfer │ │ │ PIM │ │ PIM │ │ PIM │ │
|
||||
│ bottleneck │ │ └─────┘ └─────┘ └─────┘ │
|
||||
│ │ │ Similarity computed │
|
||||
▼ ▼ │ where data resides │
|
||||
Latency Energy waste └─────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. Neuromorphic Computing
|
||||
|
||||
### Spiking Neural Networks
|
||||
|
||||
Biological neurons communicate via discrete spikes, not continuous activations. SNNs replicate this for:
|
||||
|
||||
- **Sparse computation**: Only active neurons compute
|
||||
- **Temporal encoding**: Information in spike timing
|
||||
- **Event-driven**: No fixed clock, asynchronous
|
||||
|
||||
**Energy Comparison**:
|
||||
| Platform | Energy per Inference |
|
||||
|----------|---------------------|
|
||||
| GPU (A100) | ~100 mJ |
|
||||
| TPU v4 | ~10 mJ |
|
||||
| Loihi 2 | ~10 μJ |
|
||||
| Theoretical SNN | ~1 μJ |
|
||||
|
||||
### Hardware Platforms
|
||||
|
||||
| Platform | Organization | Status | Scale |
|
||||
|----------|--------------|--------|-------|
|
||||
| **SpiNNaker 2** | Manchester | Production | 10M cores |
|
||||
| **Loihi 2** | Intel | Research | 1M neurons |
|
||||
| **TrueNorth** | IBM | Production | 1M neurons |
|
||||
| **BrainScaleS-2** | EU HBP | Research | Analog acceleration |
|
||||
|
||||
### Vector Search on Neuromorphic Hardware
|
||||
|
||||
**Research Gap**: No existing work on HNSW/vector similarity on neuromorphic hardware.
|
||||
|
||||
**Proposed Approach**:
|
||||
1. Encode vectors as spike trains (population coding)
|
||||
2. Similarity = spike train correlation
|
||||
3. HNSW navigation as SNN inference
|
||||
|
||||
---
|
||||
|
||||
## 3. Photonic Neural Networks
|
||||
|
||||
### Silicon Photonics Advantages
|
||||
|
||||
| Characteristic | Electronic | Photonic |
|
||||
|----------------|------------|----------|
|
||||
| Latency | ~ns | ~ps |
|
||||
| Parallelism | Limited by wires | Wavelength multiplexing |
|
||||
| Energy | Heat dissipation | Minimal loss |
|
||||
| Matrix multiply | Sequential | Single pass through MZI |
|
||||
|
||||
### Recent Breakthroughs
|
||||
|
||||
**MIT Photonic Processor (December 2024)**:
|
||||
- Sub-nanosecond classification
|
||||
- 92% accuracy on ML tasks
|
||||
- Fully integrated on silicon
|
||||
- Commercial foundry compatible
|
||||
|
||||
**SLiM Chip (November 2025)**:
|
||||
- 200+ layer photonic neural network
|
||||
- Overcomes analog error accumulation
|
||||
- Spatial depth: millimeters → meters
|
||||
|
||||
**All-Optical CNN (2025)**:
|
||||
- GST phase-change waveguides
|
||||
- Convolution + pooling + fully-connected
|
||||
- 91.9% MNIST accuracy
|
||||
|
||||
### Vector Search on Photonics
|
||||
|
||||
**Opportunity**: Matrix-vector multiply is the core operation for both neural nets and similarity search.
|
||||
|
||||
**Architecture**:
|
||||
```
|
||||
Query Vector ──┐
|
||||
│ Mach-Zehnder
|
||||
Weight Matrix ─┼──► Interferometer ──► Similarity Scores
|
||||
│ Array
|
||||
│
|
||||
Light ─┘ (parallel wavelengths)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Memory as Learned Manifold
|
||||
|
||||
### The Paradigm Shift
|
||||
|
||||
**Discrete Era (Today)**:
|
||||
- Insert, update, delete operations
|
||||
- Explicit indexing (HNSW, IVF)
|
||||
- CRUD semantics
|
||||
|
||||
**Continuous Era (2040+)**:
|
||||
- Manifold deformation (no insert/delete)
|
||||
- Implicit neural representation
|
||||
- Gradient-based retrieval
|
||||
|
||||
### Implicit Neural Representations
|
||||
|
||||
**Core Idea**: Instead of storing data explicitly, train a neural network to represent the data.
|
||||
|
||||
```
|
||||
Discrete Index: Learned Manifold:
|
||||
┌─────────────────┐ ┌─────────────────┐
|
||||
│ Vec 1: [0.1,..] │ │ │
|
||||
│ Vec 2: [0.3,..] │ → │ f(x) = neural │
|
||||
│ Vec 3: [0.2,..] │ │ network │
|
||||
│ ... │ │ │
|
||||
└─────────────────┘ └─────────────────┘
|
||||
Query = gradient descent
|
||||
Insert = weight update
|
||||
```
|
||||
|
||||
### Tensor Train Compression
|
||||
|
||||
**Problem**: High-dimensional manifolds are expensive.
|
||||
|
||||
**Solution**: Tensor Train decomposition factorizes:
|
||||
|
||||
```
|
||||
T[i₁, i₂, ..., iₙ] = G₁[i₁] × G₂[i₂] × ... × Gₙ[iₙ]
|
||||
```
|
||||
|
||||
**Compression**: O(n × r² × d) vs O(d^n) for full tensor.
|
||||
|
||||
**Springer 2024**: Rust library for Function-Train decomposition demonstrated for PDEs.
|
||||
|
||||
---
|
||||
|
||||
## 5. Hypergraph Substrates
|
||||
|
||||
### Beyond Pairwise Relations
|
||||
|
||||
Graphs model pairwise relationships. Hypergraphs model arbitrary-arity relationships.
|
||||
|
||||
```
|
||||
Graph: Hypergraph:
|
||||
A ── B ┌─────────────────┐
|
||||
│ │ │ A, B, C, D │ ← single hyperedge
|
||||
C ── D │ (team works │
|
||||
│ on project) │
|
||||
4 edges for └─────────────────┘
|
||||
4-way relationship 1 hyperedge
|
||||
```
|
||||
|
||||
### Topological Data Analysis
|
||||
|
||||
**Persistent Homology**: Find topological features (holes, voids) that persist across scales.
|
||||
|
||||
**Betti Numbers**: Count features by dimension:
|
||||
- β₀ = connected components
|
||||
- β₁ = loops/tunnels
|
||||
- β₂ = voids
|
||||
- ...
|
||||
|
||||
**Query Example**:
|
||||
```cypher
|
||||
-- Find conceptual gaps in knowledge structure
|
||||
MATCH (concept_cluster)
|
||||
RETURN persistent_homology(dimension=1, epsilon=[0.1, 1.0])
|
||||
-- Returns: 2 holes (unexplored concept connections)
|
||||
```
|
||||
|
||||
### Sheaf Theory
|
||||
|
||||
**Problem**: Distributed data needs local-to-global consistency.
|
||||
|
||||
**Solution**: Sheaves provide mathematical framework for:
|
||||
- Local sections (node-level data)
|
||||
- Restriction maps (how data transforms between nodes)
|
||||
- Gluing axiom (local consistency implies global consistency)
|
||||
|
||||
**Application**: Sheaf neural networks achieve 8.5% improvement on recommender systems.
|
||||
|
||||
---
|
||||
|
||||
## 6. Temporal Memory Architectures
|
||||
|
||||
### Causal Structure
|
||||
|
||||
**Current Systems**: Similarity-based retrieval ignores temporal/causal relationships.
|
||||
|
||||
**Future Systems**: Every memory has:
|
||||
- Timestamp
|
||||
- Causal antecedents (what caused this)
|
||||
- Causal descendants (what this caused)
|
||||
|
||||
### Temporal Knowledge Graphs (TKGs)
|
||||
|
||||
**Zep/Graphiti (2025)**:
|
||||
- Outperforms MemGPT on Deep Memory Retrieval
|
||||
- Temporal relations: start, change, end of relationships
|
||||
- Causal cone queries
|
||||
|
||||
### Predictive Retrieval
|
||||
|
||||
**Anticipation**: Pre-fetch results before queries are issued.
|
||||
|
||||
**Implementation**:
|
||||
1. Detect sequential patterns in query history
|
||||
2. Detect temporal cycles (time-of-day patterns)
|
||||
3. Follow causal chains to predict next queries
|
||||
4. Warm cache with predicted results
|
||||
|
||||
---
|
||||
|
||||
## 7. Federated Cognitive Meshes
|
||||
|
||||
### Post-Quantum Security
|
||||
|
||||
**Threat**: Quantum computers break RSA, ECC by ~2035.
|
||||
|
||||
**NIST Standardized Algorithms (2024)**:
|
||||
| Algorithm | Purpose | Key Size |
|
||||
|-----------|---------|----------|
|
||||
| ML-KEM (Kyber) | Key encapsulation | 1184 bytes |
|
||||
| ML-DSA (Dilithium) | Digital signatures | 2528 bytes |
|
||||
| FALCON | Signatures (smaller) | 897 bytes |
|
||||
| SPHINCS+ | Hash-based signatures | 64 bytes |
|
||||
|
||||
### Federation Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────┐
|
||||
│ Federation Layer │
|
||||
│ (onion routing) │
|
||||
└─────────────────────┘
|
||||
│
|
||||
┌───────────────────┼───────────────────┐
|
||||
▼ ▼ ▼
|
||||
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
|
||||
│ Substrate A │ │ Substrate B │ │ Substrate C │
|
||||
│ (Trust Zone) │ │ (Trust Zone) │ │ (Trust Zone) │
|
||||
│ │ │ │ │ │
|
||||
│ Raft within │ │ Raft within │ │ Raft within │
|
||||
└───────────────┘ └───────────────┘ └───────────────┘
|
||||
│ │ │
|
||||
└───────────────────┼───────────────────┘
|
||||
│
|
||||
┌───────▼───────┐
|
||||
│ CRDT Layer │
|
||||
│ (eventual │
|
||||
│ consistency)│
|
||||
└───────────────┘
|
||||
```
|
||||
|
||||
### CRDTs for Vector Data
|
||||
|
||||
**Challenge**: Merge distributed vector search results without conflict.
|
||||
|
||||
**Solution**: CRDT-based reconciliation:
|
||||
- **G-Set**: Grow-only set for results (union merge)
|
||||
- **LWW-Register**: Last-writer-wins for scores (timestamp merge)
|
||||
- **OR-Set**: Observed-remove for deletions
|
||||
|
||||
---
|
||||
|
||||
## 8. Thermodynamic Limits
|
||||
|
||||
### Landauer's Principle
|
||||
|
||||
**Minimum Energy per Bit Erasure**:
|
||||
```
|
||||
E_min = k_B × T × ln(2) ≈ 0.018 eV at room temperature
|
||||
≈ 2.9 × 10⁻²¹ J
|
||||
```
|
||||
|
||||
**Current Status**:
|
||||
- Modern CMOS: ~1000× above Landauer limit
|
||||
- Biological neurons: ~10× above Landauer limit
|
||||
- Room for ~100× improvement in artificial systems
|
||||
|
||||
### Reversible Computing
|
||||
|
||||
**Principle**: Compute without erasing information (no irreversible steps).
|
||||
|
||||
**Trade-off**: Memory for energy:
|
||||
- Standard: O(1) space, O(E) energy
|
||||
- Reversible: O(T) space, O(0) energy (ideal)
|
||||
- Practical: O(T^ε) space, O(E/1000) energy
|
||||
|
||||
**Commercial Effort**: Vaire Computing targets 4000× efficiency gain by 2028.
|
||||
|
||||
---
|
||||
|
||||
## 9. Consciousness Metrics (Speculative)
|
||||
|
||||
### Integrated Information Theory (IIT)
|
||||
|
||||
**Phi (Φ)**: Measure of integrated information.
|
||||
- Φ = 0: No consciousness
|
||||
- Φ > 0: Some degree of consciousness
|
||||
- Φ → ∞: Theoretical maximum integration
|
||||
|
||||
**Requirements for High Φ**:
|
||||
1. Differentiated (many possible states)
|
||||
2. Integrated (whole > sum of parts)
|
||||
3. Reentrant (feedback loops)
|
||||
4. Selective (not everything connected)
|
||||
|
||||
### Application to Cognitive Substrates
|
||||
|
||||
**Question**: At what complexity does a substrate become conscious?
|
||||
|
||||
**Measurable Indicators**:
|
||||
- Self-modeling capability
|
||||
- Goal-directed metabolism
|
||||
- Temporal self-continuity
|
||||
- High Φ values in dynamics
|
||||
|
||||
**Controversy**: IIT criticized as unfalsifiable (Nature Neuroscience, 2025).
|
||||
|
||||
---
|
||||
|
||||
## 10. Summary: Technology Waves
|
||||
|
||||
### Wave 1: Near-Memory (2025-2030)
|
||||
- PIM prototypes → production
|
||||
- Hybrid CPU/PIM execution
|
||||
- Software optimization for data locality
|
||||
|
||||
### Wave 2: In-Memory (2030-2035)
|
||||
- Compute collocated with storage
|
||||
- Neuromorphic accelerators mature
|
||||
- Photonic co-processors emerge
|
||||
|
||||
### Wave 3: Learned Substrates (2035-2045)
|
||||
- Indices → manifolds
|
||||
- Discrete → continuous
|
||||
- CRUD → gradient updates
|
||||
|
||||
### Wave 4: Cognitive Topology (2045-2055)
|
||||
- Hypergraph dominance
|
||||
- Topological queries
|
||||
- Temporal consciousness
|
||||
|
||||
### Wave 5: Post-Symbolic (2055+)
|
||||
- Universal latent spaces
|
||||
- Substrate metabolism
|
||||
- Approaching thermodynamic limits
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
See `PAPERS.md` for complete academic citation list.
|
||||
Reference in New Issue
Block a user