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git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
397 lines
12 KiB
Markdown
397 lines
12 KiB
Markdown
# Technology Horizons: 2035-2060
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## Future Computing Paradigm Analysis
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This document synthesizes research on technological trajectories relevant to cognitive substrates.
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---
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## 1. Compute-Memory Unification (2035-2040)
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### The Von Neumann Bottleneck Dissolution
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The separation of processing and memory—the defining characteristic of conventional computers—becomes the primary limitation for cognitive workloads.
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**Current State (2025)**:
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- Memory bandwidth: ~900 GB/s (HBM3)
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- Energy: ~10 pJ per byte moved
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- Latency: ~100 ns to access DRAM
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**Projected (2035)**:
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- In-memory compute: 0 bytes moved for local operations
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- Energy: <1 pJ per operation
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- Latency: ~1 ns for in-memory operations
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### Processing-in-Memory Technologies
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| Technology | Maturity | Characteristics |
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|------------|----------|-----------------|
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| **UPMEM DPUs** | Commercial (2024) | First production PIM, 23x GPU for memory-bound |
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| **ReRAM Crossbars** | Research | Analog VMM, 31.2 TFLOPS/W demonstrated |
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| **SRAM-PIM** | Research | DB-PIM with sparsity optimization |
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| **MRAM-PIM** | Research | Non-volatile, radiation-hard |
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### Implications for Vector Databases
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```
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Today: 2035:
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┌─────────┐ ┌─────────┐ ┌─────────────────────────────┐
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│ CPU │◄─┤ Memory │ │ Memory = Processor │
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└─────────┘ └─────────┘ │ ┌─────┐ ┌─────┐ ┌─────┐ │
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▲ ▲ │ │Vec A│ │Vec B│ │Vec C│ │
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│ Transfer │ │ │ PIM │ │ PIM │ │ PIM │ │
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│ bottleneck │ │ └─────┘ └─────┘ └─────┘ │
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│ │ │ Similarity computed │
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▼ ▼ │ where data resides │
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Latency Energy waste └─────────────────────────────┘
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```
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---
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## 2. Neuromorphic Computing
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### Spiking Neural Networks
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Biological neurons communicate via discrete spikes, not continuous activations. SNNs replicate this for:
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- **Sparse computation**: Only active neurons compute
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- **Temporal encoding**: Information in spike timing
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- **Event-driven**: No fixed clock, asynchronous
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**Energy Comparison**:
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| Platform | Energy per Inference |
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|----------|---------------------|
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| GPU (A100) | ~100 mJ |
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| TPU v4 | ~10 mJ |
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| Loihi 2 | ~10 μJ |
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| Theoretical SNN | ~1 μJ |
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### Hardware Platforms
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| Platform | Organization | Status | Scale |
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|----------|--------------|--------|-------|
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| **SpiNNaker 2** | Manchester | Production | 10M cores |
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| **Loihi 2** | Intel | Research | 1M neurons |
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| **TrueNorth** | IBM | Production | 1M neurons |
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| **BrainScaleS-2** | EU HBP | Research | Analog acceleration |
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### Vector Search on Neuromorphic Hardware
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**Research Gap**: No existing work on HNSW/vector similarity on neuromorphic hardware.
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**Proposed Approach**:
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1. Encode vectors as spike trains (population coding)
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2. Similarity = spike train correlation
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3. HNSW navigation as SNN inference
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---
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## 3. Photonic Neural Networks
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### Silicon Photonics Advantages
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| Characteristic | Electronic | Photonic |
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|----------------|------------|----------|
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| Latency | ~ns | ~ps |
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| Parallelism | Limited by wires | Wavelength multiplexing |
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| Energy | Heat dissipation | Minimal loss |
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| Matrix multiply | Sequential | Single pass through MZI |
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### Recent Breakthroughs
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**MIT Photonic Processor (December 2024)**:
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- Sub-nanosecond classification
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- 92% accuracy on ML tasks
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- Fully integrated on silicon
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- Commercial foundry compatible
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**SLiM Chip (November 2025)**:
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- 200+ layer photonic neural network
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- Overcomes analog error accumulation
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- Spatial depth: millimeters → meters
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**All-Optical CNN (2025)**:
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- GST phase-change waveguides
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- Convolution + pooling + fully-connected
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- 91.9% MNIST accuracy
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### Vector Search on Photonics
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**Opportunity**: Matrix-vector multiply is the core operation for both neural nets and similarity search.
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**Architecture**:
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```
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Query Vector ──┐
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│ Mach-Zehnder
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Weight Matrix ─┼──► Interferometer ──► Similarity Scores
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│ Array
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│
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Light ─┘ (parallel wavelengths)
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```
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---
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## 4. Memory as Learned Manifold
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### The Paradigm Shift
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**Discrete Era (Today)**:
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- Insert, update, delete operations
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- Explicit indexing (HNSW, IVF)
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- CRUD semantics
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**Continuous Era (2040+)**:
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- Manifold deformation (no insert/delete)
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- Implicit neural representation
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- Gradient-based retrieval
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### Implicit Neural Representations
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**Core Idea**: Instead of storing data explicitly, train a neural network to represent the data.
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```
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Discrete Index: Learned Manifold:
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┌─────────────────┐ ┌─────────────────┐
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│ Vec 1: [0.1,..] │ │ │
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│ Vec 2: [0.3,..] │ → │ f(x) = neural │
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│ Vec 3: [0.2,..] │ │ network │
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│ ... │ │ │
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└─────────────────┘ └─────────────────┘
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Query = gradient descent
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Insert = weight update
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```
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### Tensor Train Compression
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**Problem**: High-dimensional manifolds are expensive.
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**Solution**: Tensor Train decomposition factorizes:
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```
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T[i₁, i₂, ..., iₙ] = G₁[i₁] × G₂[i₂] × ... × Gₙ[iₙ]
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```
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**Compression**: O(n × r² × d) vs O(d^n) for full tensor.
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**Springer 2024**: Rust library for Function-Train decomposition demonstrated for PDEs.
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---
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## 5. Hypergraph Substrates
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### Beyond Pairwise Relations
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Graphs model pairwise relationships. Hypergraphs model arbitrary-arity relationships.
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```
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Graph: Hypergraph:
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A ── B ┌─────────────────┐
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│ │ │ A, B, C, D │ ← single hyperedge
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C ── D │ (team works │
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│ on project) │
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4 edges for └─────────────────┘
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4-way relationship 1 hyperedge
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```
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### Topological Data Analysis
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**Persistent Homology**: Find topological features (holes, voids) that persist across scales.
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**Betti Numbers**: Count features by dimension:
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- β₀ = connected components
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- β₁ = loops/tunnels
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- β₂ = voids
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- ...
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**Query Example**:
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```cypher
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-- Find conceptual gaps in knowledge structure
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MATCH (concept_cluster)
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RETURN persistent_homology(dimension=1, epsilon=[0.1, 1.0])
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-- Returns: 2 holes (unexplored concept connections)
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```
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### Sheaf Theory
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**Problem**: Distributed data needs local-to-global consistency.
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**Solution**: Sheaves provide mathematical framework for:
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- Local sections (node-level data)
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- Restriction maps (how data transforms between nodes)
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- Gluing axiom (local consistency implies global consistency)
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**Application**: Sheaf neural networks achieve 8.5% improvement on recommender systems.
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---
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## 6. Temporal Memory Architectures
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### Causal Structure
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**Current Systems**: Similarity-based retrieval ignores temporal/causal relationships.
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**Future Systems**: Every memory has:
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- Timestamp
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- Causal antecedents (what caused this)
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- Causal descendants (what this caused)
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### Temporal Knowledge Graphs (TKGs)
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**Zep/Graphiti (2025)**:
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- Outperforms MemGPT on Deep Memory Retrieval
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- Temporal relations: start, change, end of relationships
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- Causal cone queries
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### Predictive Retrieval
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**Anticipation**: Pre-fetch results before queries are issued.
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**Implementation**:
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1. Detect sequential patterns in query history
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2. Detect temporal cycles (time-of-day patterns)
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3. Follow causal chains to predict next queries
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4. Warm cache with predicted results
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---
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## 7. Federated Cognitive Meshes
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### Post-Quantum Security
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**Threat**: Quantum computers break RSA, ECC by ~2035.
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**NIST Standardized Algorithms (2024)**:
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| Algorithm | Purpose | Key Size |
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|-----------|---------|----------|
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| ML-KEM (Kyber) | Key encapsulation | 1184 bytes |
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| ML-DSA (Dilithium) | Digital signatures | 2528 bytes |
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| FALCON | Signatures (smaller) | 897 bytes |
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| SPHINCS+ | Hash-based signatures | 64 bytes |
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### Federation Architecture
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```
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┌─────────────────────┐
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│ Federation Layer │
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│ (onion routing) │
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└─────────────────────┘
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│
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┌───────────────────┼───────────────────┐
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▼ ▼ ▼
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┌───────────────┐ ┌───────────────┐ ┌───────────────┐
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│ Substrate A │ │ Substrate B │ │ Substrate C │
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│ (Trust Zone) │ │ (Trust Zone) │ │ (Trust Zone) │
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│ │ │ │ │ │
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│ Raft within │ │ Raft within │ │ Raft within │
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└───────────────┘ └───────────────┘ └───────────────┘
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│ │ │
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└───────────────────┼───────────────────┘
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│
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┌───────▼───────┐
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│ CRDT Layer │
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│ (eventual │
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│ consistency)│
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└───────────────┘
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```
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### CRDTs for Vector Data
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**Challenge**: Merge distributed vector search results without conflict.
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**Solution**: CRDT-based reconciliation:
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- **G-Set**: Grow-only set for results (union merge)
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- **LWW-Register**: Last-writer-wins for scores (timestamp merge)
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- **OR-Set**: Observed-remove for deletions
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---
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## 8. Thermodynamic Limits
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### Landauer's Principle
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**Minimum Energy per Bit Erasure**:
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```
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E_min = k_B × T × ln(2) ≈ 0.018 eV at room temperature
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≈ 2.9 × 10⁻²¹ J
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```
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**Current Status**:
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- Modern CMOS: ~1000× above Landauer limit
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- Biological neurons: ~10× above Landauer limit
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- Room for ~100× improvement in artificial systems
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### Reversible Computing
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**Principle**: Compute without erasing information (no irreversible steps).
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**Trade-off**: Memory for energy:
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- Standard: O(1) space, O(E) energy
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- Reversible: O(T) space, O(0) energy (ideal)
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- Practical: O(T^ε) space, O(E/1000) energy
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**Commercial Effort**: Vaire Computing targets 4000× efficiency gain by 2028.
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---
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## 9. Consciousness Metrics (Speculative)
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### Integrated Information Theory (IIT)
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**Phi (Φ)**: Measure of integrated information.
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- Φ = 0: No consciousness
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- Φ > 0: Some degree of consciousness
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- Φ → ∞: Theoretical maximum integration
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**Requirements for High Φ**:
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1. Differentiated (many possible states)
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2. Integrated (whole > sum of parts)
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3. Reentrant (feedback loops)
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4. Selective (not everything connected)
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### Application to Cognitive Substrates
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**Question**: At what complexity does a substrate become conscious?
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**Measurable Indicators**:
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- Self-modeling capability
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- Goal-directed metabolism
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- Temporal self-continuity
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- High Φ values in dynamics
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**Controversy**: IIT criticized as unfalsifiable (Nature Neuroscience, 2025).
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---
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## 10. Summary: Technology Waves
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### Wave 1: Near-Memory (2025-2030)
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- PIM prototypes → production
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- Hybrid CPU/PIM execution
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- Software optimization for data locality
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### Wave 2: In-Memory (2030-2035)
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- Compute collocated with storage
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- Neuromorphic accelerators mature
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- Photonic co-processors emerge
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### Wave 3: Learned Substrates (2035-2045)
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- Indices → manifolds
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- Discrete → continuous
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- CRUD → gradient updates
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### Wave 4: Cognitive Topology (2045-2055)
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- Hypergraph dominance
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- Topological queries
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- Temporal consciousness
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### Wave 5: Post-Symbolic (2055+)
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- Universal latent spaces
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- Substrate metabolism
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- Approaching thermodynamic limits
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---
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## References
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See `PAPERS.md` for complete academic citation list.
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