677 lines
21 KiB
Markdown
677 lines
21 KiB
Markdown
# RuVector Nervous System: Deployment Mapping & Build Order
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## Executive Summary
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This document defines the deployment architecture and three-phase build order for the RuVector Nervous System, integrating hyperdimensional computing (HDC), Modern Hopfield networks, and biologically-inspired learning with Cognitum neuromorphic hardware.
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**Key Goals:**
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- 10× energy efficiency improvement over baseline HNSW
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- Sub-millisecond inference latency
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- Exponential capacity scaling with dimension
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- Online learning with forgetting prevention
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- Deterministic safety guarantees
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---
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## Deployment Tiers
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### Tier 1: Cognitum Worker Tiles (Reflex Tier)
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**Purpose:** Ultra-low-latency event processing and reflexive responses
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**Components Deployed:**
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- Event ingestion pipeline
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- K-WTA selection circuits
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- Dendritic coincidence detection
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- BTSP one-shot learning gates
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- Hard safety validators
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- Bounded event queues
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**Hardware Constraints:**
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- **Memory:** On-tile SRAM only (no external DRAM access)
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- **Bandwidth:** Zero off-tile memory bandwidth during reflex path
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- **Timing:** Deterministic execution with hard bounds
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- **Queue Depth:** Fixed-size circular buffers (configurable, e.g., 256 events)
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**Operational Characteristics:**
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- **Latency Target:** <100μs event→action
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- **Energy Target:** <1μJ per query
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- **Sparsity:** 2-5% neuron activation
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- **Determinism:** Maximum iteration counts enforced
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**Safety Mechanisms:**
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- Hard timeout enforcement (circuit breaker)
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- Input validation gates
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- Witness logging for all safety-critical decisions
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- Automatic fallback to safe default state
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---
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### Tier 2: Cognitum Hub (Coordinator Cores)
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**Purpose:** Cross-tile coordination and plasticity consolidation
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**Components Deployed:**
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- Routing decision logic
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- Plasticity consolidation engine (EWC, CLS)
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- Workspace coordinator (Global Workspace Theory)
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- Coherence-gated routing
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- Inter-tile communication manager
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**Memory Architecture:**
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- **L1/L2:** Per-core cache for hot paths
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- **L3:** Coherent shared cache across hub cores
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- **Access Pattern:** Cache-friendly sequential scans for consolidation
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**Operational Characteristics:**
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- **Latency Target:** <10ms for consolidation operations
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- **Bandwidth:** High coherent bandwidth for multi-tile sync
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- **Plasticity Rate:** Capped updates per second (e.g., 1000 updates/sec)
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- **Coordination:** Supports up to 64 worker tiles per hub
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**Safety Mechanisms:**
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- Rate limiting on plasticity updates
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- Threshold versioning for rollback capability
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- Coherence validation before routing decisions
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- Circuit breakers for latency spikes
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---
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### Tier 3: RuVector Server
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**Purpose:** Long-horizon learning and associative memory
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**Components Deployed:**
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- Modern Hopfield associative memory
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- HDC pattern separation encoding
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- Continuous Learning with Synaptic Intelligence (CLS)
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- Elastic Weight Consolidation (EWC)
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- Cross-collection analytics
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- Predictive residual learner
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**Memory Architecture:**
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- **Storage:** Large-scale vector embeddings in memory
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- **Cache:** Hot pattern cache for frequently accessed memories
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- **Compute:** GPU/SIMD acceleration for Hopfield energy minimization
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- **Persistence:** Periodic snapshots to RuVector Postgres
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**Operational Characteristics:**
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- **Latency Target:** <10ms for associative retrieval
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- **Capacity:** Exponential(d) with dimension d
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- **Learning:** Online updates with forgetting prevention
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- **Sparsity:** 2-5% activation via K-WTA
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**Safety Mechanisms:**
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- Predictive residual thresholds prevent spurious writes
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- EWC prevents catastrophic forgetting
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- Collection versioning for rollback
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- Automatic fallback to baseline HNSW on failures
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---
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### Tier 4: RuVector Postgres
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**Purpose:** Durable storage and collection parameter versioning
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**Components Deployed:**
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- Collection metadata and parameters
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- Threshold versioning (predictive residual gates)
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- BTSP one-shot association windows
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- Long-term trajectory logs
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- Performance metrics and analytics
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**Storage Schema:**
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```sql
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-- Collection versioning
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collections (
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id UUID PRIMARY KEY,
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version INT NOT NULL,
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created_at TIMESTAMP,
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hdc_dimension INT,
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hopfield_beta FLOAT,
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kWTA_k INT,
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predictive_threshold FLOAT
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);
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-- BTSP association windows
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btsp_windows (
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collection_id UUID REFERENCES collections(id),
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window_start TIMESTAMP,
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window_end TIMESTAMP,
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max_one_shot_associations INT,
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associations_used INT
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);
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-- Witness logs (safety-critical decisions)
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witness_logs (
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timestamp TIMESTAMP,
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component VARCHAR(50),
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input_hash BYTEA,
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output_hash BYTEA,
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decision VARCHAR(20),
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latency_us INT
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);
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-- Performance metrics
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metrics (
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timestamp TIMESTAMP,
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tier VARCHAR(20),
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operation VARCHAR(50),
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latency_p50_ms FLOAT,
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latency_p99_ms FLOAT,
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energy_uj FLOAT,
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success_rate FLOAT
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);
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```
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**Operational Characteristics:**
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- **Write Pattern:** Gated writes via predictive residual
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- **Read Pattern:** Hot parameter cache in RuVector Server
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- **Versioning:** Immutable collection versions with rollback
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- **Analytics:** Aggregated metrics for performance monitoring
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**Safety Mechanisms:**
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- Immutable version history
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- Atomic parameter updates
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- Witness log retention for audit trails
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- Circuit breaker configuration persistence
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---
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## Three-Phase Build Order
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### Phase 1: RuVector Foundation (Months 0-3)
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**Objective:** Establish core hyperdimensional and Hopfield primitives with 10× energy efficiency
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**Deliverables:**
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1. **HDC Module Complete**
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- Hypervector encoding (bundle, bind, permute)
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- K-WTA selection with configurable k
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- Similarity measurement (Hamming, cosine)
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- Integration with ruvector-core Rust API
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2. **Modern Hopfield Retrieval**
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- Energy minimization via softmax attention
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- Exponential capacity scaling
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- GPU/SIMD-accelerated inference
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- Benchmarked against baseline HNSW
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3. **K-WTA Selection**
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- Top-k neuron activation
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- Sparsity enforcement (2-5% target)
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- Hardware-friendly implementation
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- Latency <100μs for d=10000
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4. **Pattern Separation Encoding**
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- Input→hypervector encoding
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- Collision resistance validation
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- Dimensionality reduction benchmarks
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5. **Integration with ruvector-core**
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- Rust bindings for HDC and Hopfield
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- Unified query API (HNSW + HDC + Hopfield lanes)
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- Performance regression tests
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**Success Criteria:**
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- ✅ 10× energy efficiency vs baseline HNSW
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- ✅ <1ms inference latency for d=10000
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- ✅ Exponential capacity demonstrated (>1M patterns)
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- ✅ 95% retrieval accuracy on standard benchmarks
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**Demo:**
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Hybrid search system demonstrating:
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- HNSW lane for precise nearest neighbor
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- HDC lane for robust pattern matching
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- Hopfield lane for associative completion
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- Automatic lane selection based on query type
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**Risks & Mitigations:**
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- **Risk:** SIMD optimization complexity
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- **Mitigation:** Start with naive implementation, profile, optimize hot paths
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- **Risk:** Hopfield capacity limits
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- **Mitigation:** Benchmark capacity scaling empirically, document limits
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- **Risk:** Integration complexity with existing ruvector-core
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- **Mitigation:** Incremental integration with feature flags
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---
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### Phase 2: Cognitum Reflex (Months 3-6)
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**Objective:** Deploy ultra-low-latency reflex tier on Cognitum neuromorphic tiles
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**Deliverables:**
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1. **Event Bus with Bounded Queues**
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- Fixed-size circular buffers (e.g., 256 events)
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- Priority-based event scheduling
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- Overflow handling with graceful degradation
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- Zero dynamic allocation
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2. **Dendritic Coincidence Detection**
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- Multi-branch dendritic computation
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- Spatial and temporal coincidence detection
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- Threshold-based gating
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- On-tile SRAM-only implementation
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3. **BTSP One-Shot Learning**
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- Single-exposure association formation
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- Time-windowed eligibility traces
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- Gated by predictive residual
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- Postgres-backed association windows
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4. **Reflex Tier Deployment on Cognitum Tiles**
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- Tile-local event processing
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- Deterministic timing enforcement
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- Hard timeout circuits
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- Witness logging for safety gates
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**Success Criteria:**
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- ✅ <100μs event→action latency
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- ✅ <1μJ energy per query
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- ✅ 100% deterministic timing (no dynamic allocation)
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- ✅ Zero off-tile memory access in reflex path
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**Demo:**
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Real-time event processing on simulated Cognitum environment:
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- High-frequency event stream (10kHz)
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- Sub-100μs reflexive responses
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- BTSP one-shot learning demonstration
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- Safety gate validation under adversarial input
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**Risks & Mitigations:**
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- **Risk:** Cognitum hardware availability
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- **Mitigation:** Develop on cycle-accurate simulator, validate on hardware when available
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- **Risk:** SRAM capacity limits
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- **Mitigation:** Profile memory usage, optimize data structures, prune cold paths
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- **Risk:** Deterministic timing violations
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- **Mitigation:** Static analysis of loop bounds, hard timeout enforcement
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- **Risk:** BTSP stability under noise
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- **Mitigation:** Threshold tuning, windowed eligibility traces
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---
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### Phase 3: Online Learning & Coherence (Months 6-12)
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**Objective:** Distributed online learning with forgetting prevention and multi-chip coordination
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**Deliverables:**
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1. **E-prop Online Learning**
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- Eligibility trace-based gradient estimation
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- Event-driven weight updates
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- Sparse credit assignment
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- Integrated with reflex tier
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2. **EWC Consolidation**
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- Fisher Information Matrix estimation
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- Importance-weighted regularization
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- Per-collection consolidation
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- Prevents catastrophic forgetting (<5% degradation)
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3. **Coherence-Gated Routing**
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- Global Workspace Theory (GWT) coordination
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- Multi-tile coherence validation
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- Routing decisions based on workspace state
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- Hub-mediated coordination
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4. **Global Workspace Coordination**
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- Cross-tile broadcast of salient events
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- Winner-take-all workspace selection
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- Attention-based routing
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- Coherent state synchronization
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5. **Multi-Chip Cognitum Coordination**
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- Inter-chip communication protocol
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- Distributed plasticity updates
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- Fault tolerance and graceful degradation
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- Scalability to 4+ chips
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**Success Criteria:**
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- ✅ Online learning without centralized consolidation
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- ✅ <5% performance degradation over 1M updates
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- ✅ Coherent routing across 64+ tiles
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- ✅ Multi-chip coordination with <1ms sync latency
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**Demo:**
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Continuous learning demonstration:
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- 1M+ online updates without catastrophic forgetting
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- Cross-tile coherence maintained under load
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- Multi-chip coordination with graceful degradation
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- EWC prevents forgetting of critical patterns
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**Risks & Mitigations:**
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- **Risk:** E-prop stability under distribution shift
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- **Mitigation:** Adaptive learning rates, eligibility trace decay tuning
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- **Risk:** EWC computational overhead
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- **Mitigation:** Sparse Fisher approximation, periodic consolidation
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- **Risk:** Coherence protocol deadlocks
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- **Mitigation:** Timeout-based fallback, formal verification of protocol
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- **Risk:** Multi-chip synchronization overhead
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- **Mitigation:** Asynchronous updates with eventual consistency
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---
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## Risk Controls & Safety Mechanisms
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### Deterministic Bounds
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**Principle:** Every reflex path has a provable maximum execution time
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**Implementation:**
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- **Static Loop Bounds:** All loops have compile-time maximum iteration counts
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- **Hard Timeouts:** Circuit breakers enforce timeouts at hardware level
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- **No Dynamic Allocation:** Zero heap allocation in reflex paths
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- **Bounded Queues:** Fixed-size event queues with overflow handling
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**Verification:**
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- Static analysis tools verify loop bounds
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- Runtime assertions validate timeout enforcement
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- Continuous integration tests measure worst-case execution time
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---
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### Witness Logging
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**Principle:** All safety-relevant decisions are logged for audit and debugging
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**Logged Events:**
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- **Safety Gate Decisions:** Input hash, output hash, decision (accept/reject)
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- **Timestamps:** High-resolution timestamps for causality tracking
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- **Latencies:** Per-operation latency for anomaly detection
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- **Component ID:** Which tier/tile made the decision
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**Storage:**
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- Critical decisions → RuVector Postgres (durable)
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- High-frequency events → Ring buffer in RuVector Server (ephemeral)
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- Aggregated metrics → Postgres (hourly rollup)
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**Usage:**
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- Post-incident analysis
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- Continuous validation of safety properties
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- Training data for predictive models
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---
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### Rate Limiting
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**Principle:** Plasticity updates are capped to prevent divergence under adversarial input
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**Limits:**
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- **Per-Tile:** Max 1000 updates/sec per worker tile
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- **Per-Collection:** Max 10000 updates/sec across all tiles
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- **BTSP Windows:** Max 100 one-shot associations per window (e.g., 1-second windows)
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**Enforcement:**
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- Token bucket rate limiter in Cognitum Hub
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- Postgres-backed BTSP window tracking
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- Automatic throttling with graceful degradation
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**Monitoring:**
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- Alert on rate limit violations
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- Metrics track throttling frequency
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- Adaptive threshold tuning based on load
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---
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### Threshold Versioning
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**Principle:** Predictive residual thresholds are versioned with collections for rollback
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**Implementation:**
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- **Immutable Versions:** Each collection version has frozen thresholds
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- **Rollback Capability:** Revert to previous version on performance degradation
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- **A/B Testing:** Run multiple threshold versions in parallel
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- **Gradual Rollout:** Canary deployments for new thresholds
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**Schema:**
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```sql
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collection_thresholds (
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collection_id UUID,
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version INT,
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predictive_residual_threshold FLOAT,
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btsp_eligibility_threshold FLOAT,
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kWTA_k INT,
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PRIMARY KEY (collection_id, version)
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);
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```
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**Usage:**
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- Automatic rollback on >10% performance degradation
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- Manual rollback for debugging
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- Threshold evolution tracking over time
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---
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### Circuit Breakers
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**Principle:** Automatic fallback to baseline HNSW on failures or latency spikes
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**Triggers:**
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- **Latency:** p99 latency >2× target for 10 consecutive queries
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- **Error Rate:** >5% query failures in 1-second window
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- **Safety Gate:** Any hard safety timeout violation
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- **Resource Exhaustion:** Queue overflow, memory pressure
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**Fallback Behavior:**
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- Disable HDC/Hopfield lanes, route all queries to HNSW
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- Log circuit breaker activation with full context
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- Notify monitoring system for manual investigation
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- Automatic reset after cooldown period (e.g., 60 seconds)
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**Configuration:**
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- Per-collection circuit breaker settings
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- Stored in RuVector Postgres
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- Hot-reloadable without service restart
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---
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## Performance Targets Summary
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| Metric | Target | Phase | Verification Method |
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|--------|--------|-------|---------------------|
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| **Inference Latency** | <1ms | Phase 1 | Benchmark suite (p99) |
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| **Energy per Query** | <1μJ | Phase 2 | Cognitum power profiler |
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| **One-Shot Learning** | Single exposure | Phase 2 | BTSP accuracy tests |
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| **Forgetting Prevention** | <5% degradation | Phase 3 | EWC consolidation tests |
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| **Capacity Scaling** | Exponential(d) | Phase 1 | Hopfield capacity benchmark |
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| **Sparsity** | 2-5% activation | Phase 1 | K-WTA profiling |
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| **Reflex Latency** | <100μs | Phase 2 | Tile-level timing analysis |
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| **Multi-Tile Coherence** | <1ms sync | Phase 3 | Hub coordination profiler |
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| **Safety Gate Violations** | 0 per 1M queries | All | Witness log analysis |
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| **Circuit Breaker Rate** | <0.1% of queries | All | Monitoring dashboard |
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---
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## Integration with Cognitum Hardware
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### Cognitum v0 (Simulation)
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**Capabilities:**
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- Cycle-accurate simulation of tile architecture
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- SRAM modeling with realistic latencies
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- Event bus simulation with timing
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- Power estimation models
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**Usage:**
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- Phase 1-2 development and validation
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- Performance profiling before hardware availability
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- Regression testing for deterministic timing
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**Limitations:**
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- No real power measurements (estimates only)
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- Simulation overhead limits scale testing
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- May miss hardware-specific edge cases
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---
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### Cognitum v1 (Hardware)
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**Capabilities:**
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- Physical neuromorphic tiles with on-tile SRAM
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- Real power measurements (<1μJ per query target)
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- Hardware-enforced deterministic timing
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- Multi-chip interconnect for scaling
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**Usage:**
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- Phase 2-3 deployment and validation
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- Real-world power and latency measurements
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- Multi-chip scaling experiments
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- Safety-critical deployment validation
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**Requirements:**
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- Tile firmware with reflex path implementation
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- Hub software for coordination and consolidation
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- Interconnect drivers for multi-chip communication
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- Monitoring and instrumentation infrastructure
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---
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## Deployment Workflow
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### Development Workflow
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1. **Local Development**
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- RuVector Server runs on developer workstation
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- Mock Cognitum simulator for reflex tier
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- Local Postgres for persistence
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- Unit tests + integration tests
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2. **Staging Environment**
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- RuVector Server on dedicated server
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- Cognitum v0 simulator at scale
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- Staging Postgres with production-like data
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- Performance regression tests
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3. **Production Deployment**
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- RuVector Server on high-memory server (128GB+)
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- Cognitum v1 hardware tiles
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- Production Postgres with replication
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- Full monitoring and alerting
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---
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### Deployment Checklist
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**Phase 1 (RuVector Foundation):**
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- [ ] HDC module passes all unit tests
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- [ ] Hopfield capacity scaling validated
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- [ ] K-WTA latency <100μs for d=10000
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- [ ] 10× energy efficiency vs baseline HNSW
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- [ ] Integration tests with ruvector-core pass
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- [ ] Hybrid search demo functional
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**Phase 2 (Cognitum Reflex):**
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- [ ] Event bus handles 10kHz input stream
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- [ ] Reflex latency <100μs (p99)
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- [ ] BTSP one-shot learning accuracy >90%
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- [ ] Zero off-tile memory access verified
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- [ ] Witness logging functional
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- [ ] Circuit breakers tested under load
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||
|
||
**Phase 3 (Online Learning & Coherence):**
|
||
- [ ] E-prop online learning stable over 1M updates
|
||
- [ ] EWC prevents >5% forgetting
|
||
- [ ] Multi-tile coherence <1ms sync latency
|
||
- [ ] Multi-chip coordination functional
|
||
- [ ] Rate limiting prevents divergence
|
||
- [ ] Threshold versioning and rollback tested
|
||
|
||
---
|
||
|
||
## Monitoring & Observability
|
||
|
||
### Key Metrics
|
||
|
||
**Latency:**
|
||
- p50, p95, p99, p999 latency per tier
|
||
- Breakdown by operation (encode, retrieve, consolidate)
|
||
- Time-series visualization with anomaly detection
|
||
|
||
**Throughput:**
|
||
- Queries per second per tier
|
||
- Event processing rate (reflex tier)
|
||
- Plasticity updates per second
|
||
|
||
**Resource Utilization:**
|
||
- CPU, memory, disk usage per tier
|
||
- SRAM usage on Cognitum tiles
|
||
- Postgres connection pool utilization
|
||
|
||
**Safety:**
|
||
- Circuit breaker activation rate
|
||
- Safety gate violation count (target: 0)
|
||
- Rate limiter throttling frequency
|
||
|
||
**Learning:**
|
||
- BTSP association success rate
|
||
- EWC consolidation loss
|
||
- Forgetting rate over time
|
||
|
||
---
|
||
|
||
### Alerting Thresholds
|
||
|
||
**Critical Alerts:**
|
||
- Safety gate violation (immediate page)
|
||
- Circuit breaker activation (immediate notification)
|
||
- p99 latency >10× target (immediate notification)
|
||
- Error rate >5% (immediate notification)
|
||
|
||
**Warning Alerts:**
|
||
- p99 latency >2× target
|
||
- Rate limiter throttling >1% of requests
|
||
- Memory usage >80%
|
||
- BTSP association success rate <80%
|
||
|
||
---
|
||
|
||
## Appendix: Component Mapping Reference
|
||
|
||
### RuVector Core Components → Deployment Tiers
|
||
|
||
| Component | Tier | Rationale |
|
||
|-----------|------|-----------|
|
||
| HDC Encoding | Tier 1 (Cognitum Tiles) | Deterministic, SRAM-friendly |
|
||
| K-WTA Selection | Tier 1 (Cognitum Tiles) | Low-latency, sparse activation |
|
||
| Dendritic Coincidence | Tier 1 (Cognitum Tiles) | Event-driven, reflex path |
|
||
| BTSP One-Shot | Tier 1 (Cognitum Tiles) | Single-exposure learning |
|
||
| Hopfield Retrieval | Tier 3 (RuVector Server) | Large memory, GPU acceleration |
|
||
| EWC Consolidation | Tier 2 (Cognitum Hub) | Cross-tile coordination |
|
||
| E-prop Learning | Tier 2 (Cognitum Hub) | Plasticity management |
|
||
| Workspace Coordination | Tier 2 (Cognitum Hub) | Multi-tile routing |
|
||
| Predictive Residual | Tier 3 (RuVector Server) | Requires historical data |
|
||
| Collection Versioning | Tier 4 (Postgres) | Durable storage |
|
||
| Witness Logging | Tier 4 (Postgres) | Audit trail persistence |
|
||
|
||
---
|
||
|
||
## Glossary
|
||
|
||
- **BTSP:** Behavioral Timescale Synaptic Plasticity (one-shot learning)
|
||
- **CLS:** Continuous Learning with Synaptic Intelligence
|
||
- **EWC:** Elastic Weight Consolidation (forgetting prevention)
|
||
- **E-prop:** Eligibility Propagation (online learning)
|
||
- **GWT:** Global Workspace Theory (multi-agent coordination)
|
||
- **HDC:** Hyperdimensional Computing
|
||
- **K-WTA:** K-Winners-Take-All (sparse activation)
|
||
- **SRAM:** Static Random-Access Memory (on-chip memory)
|
||
|
||
---
|
||
|
||
## References
|
||
|
||
1. Cognitum Neuromorphic Hardware Architecture (Internal)
|
||
2. Modern Hopfield Networks: https://arxiv.org/abs/2008.02217
|
||
3. Hyperdimensional Computing: https://arxiv.org/abs/2111.06077
|
||
4. Elastic Weight Consolidation: https://arxiv.org/abs/1612.00796
|
||
5. E-prop Learning: https://www.nature.com/articles/s41467-020-17236-y
|
||
6. Global Workspace Theory: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5924785/
|
||
|
||
---
|
||
|
||
**Document Version:** 1.0
|
||
**Last Updated:** 2025-12-28
|
||
**Maintainer:** RuVector Nervous System Architecture Team
|