deployment.md

RuVector Nervous System: Deployment Mapping & Build Order

Executive Summary

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.

Key Goals:

• 10× energy efficiency improvement over baseline HNSW
• Sub-millisecond inference latency
• Exponential capacity scaling with dimension
• Online learning with forgetting prevention
• Deterministic safety guarantees

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Deployment Tiers

Tier 1: Cognitum Worker Tiles (Reflex Tier)

Purpose: Ultra-low-latency event processing and reflexive responses

Components Deployed:

• Event ingestion pipeline
• K-WTA selection circuits
• Dendritic coincidence detection
• BTSP one-shot learning gates
• Hard safety validators
• Bounded event queues

Hardware Constraints:

• Memory: On-tile SRAM only (no external DRAM access)
• Bandwidth: Zero off-tile memory bandwidth during reflex path
• Timing: Deterministic execution with hard bounds
• Queue Depth: Fixed-size circular buffers (configurable, e.g., 256 events)

Operational Characteristics:

• Latency Target: <100μs event→action
• Energy Target: <1μJ per query
• Sparsity: 2-5% neuron activation
• Determinism: Maximum iteration counts enforced

Safety Mechanisms:

• Hard timeout enforcement (circuit breaker)
• Input validation gates
• Witness logging for all safety-critical decisions
• Automatic fallback to safe default state

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Tier 2: Cognitum Hub (Coordinator Cores)

Purpose: Cross-tile coordination and plasticity consolidation

Components Deployed:

• Routing decision logic
• Plasticity consolidation engine (EWC, CLS)
• Workspace coordinator (Global Workspace Theory)
• Coherence-gated routing
• Inter-tile communication manager

Memory Architecture:

• L1/L2: Per-core cache for hot paths
• L3: Coherent shared cache across hub cores
• Access Pattern: Cache-friendly sequential scans for consolidation

Operational Characteristics:

• Latency Target: <10ms for consolidation operations
• Bandwidth: High coherent bandwidth for multi-tile sync
• Plasticity Rate: Capped updates per second (e.g., 1000 updates/sec)
• Coordination: Supports up to 64 worker tiles per hub

Safety Mechanisms:

• Rate limiting on plasticity updates
• Threshold versioning for rollback capability
• Coherence validation before routing decisions
• Circuit breakers for latency spikes

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Tier 3: RuVector Server

Purpose: Long-horizon learning and associative memory

Components Deployed:

• Modern Hopfield associative memory
• HDC pattern separation encoding
• Continuous Learning with Synaptic Intelligence (CLS)
• Elastic Weight Consolidation (EWC)
• Cross-collection analytics
• Predictive residual learner

Memory Architecture:

• Storage: Large-scale vector embeddings in memory
• Cache: Hot pattern cache for frequently accessed memories
• Compute: GPU/SIMD acceleration for Hopfield energy minimization
• Persistence: Periodic snapshots to RuVector Postgres

Operational Characteristics:

• Latency Target: <10ms for associative retrieval
• Capacity: Exponential(d) with dimension d
• Learning: Online updates with forgetting prevention
• Sparsity: 2-5% activation via K-WTA

Safety Mechanisms:

• Predictive residual thresholds prevent spurious writes
• EWC prevents catastrophic forgetting
• Collection versioning for rollback
• Automatic fallback to baseline HNSW on failures

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Tier 4: RuVector Postgres

Purpose: Durable storage and collection parameter versioning

Components Deployed:

• Collection metadata and parameters
• Threshold versioning (predictive residual gates)
• BTSP one-shot association windows
• Long-term trajectory logs
• Performance metrics and analytics

Storage Schema:

-- Collection versioning
collections (
  id UUID PRIMARY KEY,
  version INT NOT NULL,
  created_at TIMESTAMP,
  hdc_dimension INT,
  hopfield_beta FLOAT,
  kWTA_k INT,
  predictive_threshold FLOAT
);

-- BTSP association windows
btsp_windows (
  collection_id UUID REFERENCES collections(id),
  window_start TIMESTAMP,
  window_end TIMESTAMP,
  max_one_shot_associations INT,
  associations_used INT
);

-- Witness logs (safety-critical decisions)
witness_logs (
  timestamp TIMESTAMP,
  component VARCHAR(50),
  input_hash BYTEA,
  output_hash BYTEA,
  decision VARCHAR(20),
  latency_us INT
);

-- Performance metrics
metrics (
  timestamp TIMESTAMP,
  tier VARCHAR(20),
  operation VARCHAR(50),
  latency_p50_ms FLOAT,
  latency_p99_ms FLOAT,
  energy_uj FLOAT,
  success_rate FLOAT
);

Operational Characteristics:

• Write Pattern: Gated writes via predictive residual
• Read Pattern: Hot parameter cache in RuVector Server
• Versioning: Immutable collection versions with rollback
• Analytics: Aggregated metrics for performance monitoring

Safety Mechanisms:

• Immutable version history
• Atomic parameter updates
• Witness log retention for audit trails
• Circuit breaker configuration persistence

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Three-Phase Build Order

Phase 1: RuVector Foundation (Months 0-3)

Objective: Establish core hyperdimensional and Hopfield primitives with 10× energy efficiency

Deliverables:

1. HDC Module Complete

   • Hypervector encoding (bundle, bind, permute)
   • K-WTA selection with configurable k
   • Similarity measurement (Hamming, cosine)
   • Integration with ruvector-core Rust API

2. Modern Hopfield Retrieval

   • Energy minimization via softmax attention
   • Exponential capacity scaling
   • GPU/SIMD-accelerated inference
   • Benchmarked against baseline HNSW

3. K-WTA Selection

   • Top-k neuron activation
   • Sparsity enforcement (2-5% target)
   • Hardware-friendly implementation
   • Latency <100μs for d=10000

4. Pattern Separation Encoding

   • Input→hypervector encoding
   • Collision resistance validation
   • Dimensionality reduction benchmarks

5. Integration with ruvector-core

   • Rust bindings for HDC and Hopfield
   • Unified query API (HNSW + HDC + Hopfield lanes)
   • Performance regression tests

Success Criteria:

• ✅ 10× energy efficiency vs baseline HNSW
• ✅ <1ms inference latency for d=10000
• ✅ Exponential capacity demonstrated (>1M patterns)
• ✅ 95% retrieval accuracy on standard benchmarks

Demo: Hybrid search system demonstrating:

• HNSW lane for precise nearest neighbor
• HDC lane for robust pattern matching
• Hopfield lane for associative completion
• Automatic lane selection based on query type

Risks & Mitigations:

• Risk: SIMD optimization complexity
  • Mitigation: Start with naive implementation, profile, optimize hot paths
• Risk: Hopfield capacity limits
  • Mitigation: Benchmark capacity scaling empirically, document limits
• Risk: Integration complexity with existing ruvector-core
  • Mitigation: Incremental integration with feature flags

---

Phase 2: Cognitum Reflex (Months 3-6)

Objective: Deploy ultra-low-latency reflex tier on Cognitum neuromorphic tiles

Deliverables:

1. Event Bus with Bounded Queues

   • Fixed-size circular buffers (e.g., 256 events)
   • Priority-based event scheduling
   • Overflow handling with graceful degradation
   • Zero dynamic allocation

2. Dendritic Coincidence Detection

   • Multi-branch dendritic computation
   • Spatial and temporal coincidence detection
   • Threshold-based gating
   • On-tile SRAM-only implementation

3. BTSP One-Shot Learning

   • Single-exposure association formation
   • Time-windowed eligibility traces
   • Gated by predictive residual
   • Postgres-backed association windows

4. Reflex Tier Deployment on Cognitum Tiles

   • Tile-local event processing
   • Deterministic timing enforcement
   • Hard timeout circuits
   • Witness logging for safety gates

Success Criteria:

• ✅ <100μs event→action latency
• ✅ <1μJ energy per query
• ✅ 100% deterministic timing (no dynamic allocation)
• ✅ Zero off-tile memory access in reflex path

Demo: Real-time event processing on simulated Cognitum environment:

• High-frequency event stream (10kHz)
• Sub-100μs reflexive responses
• BTSP one-shot learning demonstration
• Safety gate validation under adversarial input

Risks & Mitigations:

• Risk: Cognitum hardware availability
  • Mitigation: Develop on cycle-accurate simulator, validate on hardware when available
• Risk: SRAM capacity limits
  • Mitigation: Profile memory usage, optimize data structures, prune cold paths
• Risk: Deterministic timing violations
  • Mitigation: Static analysis of loop bounds, hard timeout enforcement
• Risk: BTSP stability under noise
  • Mitigation: Threshold tuning, windowed eligibility traces

---

Phase 3: Online Learning & Coherence (Months 6-12)

Objective: Distributed online learning with forgetting prevention and multi-chip coordination

Deliverables:

1. E-prop Online Learning

   • Eligibility trace-based gradient estimation
   • Event-driven weight updates
   • Sparse credit assignment
   • Integrated with reflex tier

2. EWC Consolidation

   • Fisher Information Matrix estimation
   • Importance-weighted regularization
   • Per-collection consolidation
   • Prevents catastrophic forgetting (<5% degradation)

3. Coherence-Gated Routing

   • Global Workspace Theory (GWT) coordination
   • Multi-tile coherence validation
   • Routing decisions based on workspace state
   • Hub-mediated coordination

4. Global Workspace Coordination

   • Cross-tile broadcast of salient events
   • Winner-take-all workspace selection
   • Attention-based routing
   • Coherent state synchronization

5. Multi-Chip Cognitum Coordination

   • Inter-chip communication protocol
   • Distributed plasticity updates
   • Fault tolerance and graceful degradation
   • Scalability to 4+ chips

Success Criteria:

• ✅ Online learning without centralized consolidation
• ✅ <5% performance degradation over 1M updates
• ✅ Coherent routing across 64+ tiles
• ✅ Multi-chip coordination with <1ms sync latency

Demo: Continuous learning demonstration:

• 1M+ online updates without catastrophic forgetting
• Cross-tile coherence maintained under load
• Multi-chip coordination with graceful degradation
• EWC prevents forgetting of critical patterns

Risks & Mitigations:

• Risk: E-prop stability under distribution shift
  • Mitigation: Adaptive learning rates, eligibility trace decay tuning
• Risk: EWC computational overhead
  • Mitigation: Sparse Fisher approximation, periodic consolidation
• Risk: Coherence protocol deadlocks
  • Mitigation: Timeout-based fallback, formal verification of protocol
• Risk: Multi-chip synchronization overhead
  • Mitigation: Asynchronous updates with eventual consistency

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Risk Controls & Safety Mechanisms

Deterministic Bounds

Principle: Every reflex path has a provable maximum execution time

Implementation:

• Static Loop Bounds: All loops have compile-time maximum iteration counts
• Hard Timeouts: Circuit breakers enforce timeouts at hardware level
• No Dynamic Allocation: Zero heap allocation in reflex paths
• Bounded Queues: Fixed-size event queues with overflow handling

Verification:

• Static analysis tools verify loop bounds
• Runtime assertions validate timeout enforcement
• Continuous integration tests measure worst-case execution time

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Witness Logging

Principle: All safety-relevant decisions are logged for audit and debugging

Logged Events:

• Safety Gate Decisions: Input hash, output hash, decision (accept/reject)
• Timestamps: High-resolution timestamps for causality tracking
• Latencies: Per-operation latency for anomaly detection
• Component ID: Which tier/tile made the decision

Storage:

• Critical decisions → RuVector Postgres (durable)
• High-frequency events → Ring buffer in RuVector Server (ephemeral)
• Aggregated metrics → Postgres (hourly rollup)

Usage:

• Post-incident analysis
• Continuous validation of safety properties
• Training data for predictive models

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Rate Limiting

Principle: Plasticity updates are capped to prevent divergence under adversarial input

Limits:

• Per-Tile: Max 1000 updates/sec per worker tile
• Per-Collection: Max 10000 updates/sec across all tiles
• BTSP Windows: Max 100 one-shot associations per window (e.g., 1-second windows)

Enforcement:

• Token bucket rate limiter in Cognitum Hub
• Postgres-backed BTSP window tracking
• Automatic throttling with graceful degradation

Monitoring:

• Alert on rate limit violations
• Metrics track throttling frequency
• Adaptive threshold tuning based on load

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Threshold Versioning

Principle: Predictive residual thresholds are versioned with collections for rollback

Implementation:

• Immutable Versions: Each collection version has frozen thresholds
• Rollback Capability: Revert to previous version on performance degradation
• A/B Testing: Run multiple threshold versions in parallel
• Gradual Rollout: Canary deployments for new thresholds

Schema:

collection_thresholds (
  collection_id UUID,
  version INT,
  predictive_residual_threshold FLOAT,
  btsp_eligibility_threshold FLOAT,
  kWTA_k INT,
  PRIMARY KEY (collection_id, version)
);

Usage:

• Automatic rollback on >10% performance degradation
• Manual rollback for debugging
• Threshold evolution tracking over time

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Circuit Breakers

Principle: Automatic fallback to baseline HNSW on failures or latency spikes

Triggers:

• Latency: p99 latency >2× target for 10 consecutive queries
• Error Rate: >5% query failures in 1-second window
• Safety Gate: Any hard safety timeout violation
• Resource Exhaustion: Queue overflow, memory pressure

Fallback Behavior:

• Disable HDC/Hopfield lanes, route all queries to HNSW
• Log circuit breaker activation with full context
• Notify monitoring system for manual investigation
• Automatic reset after cooldown period (e.g., 60 seconds)

Configuration:

• Per-collection circuit breaker settings
• Stored in RuVector Postgres
• Hot-reloadable without service restart

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Performance Targets Summary

Metric	Target	Phase	Verification Method
Inference Latency	<1ms	Phase 1	Benchmark suite (p99)
Energy per Query	<1μJ	Phase 2	Cognitum power profiler
One-Shot Learning	Single exposure	Phase 2	BTSP accuracy tests
Forgetting Prevention	<5% degradation	Phase 3	EWC consolidation tests
Capacity Scaling	Exponential(d)	Phase 1	Hopfield capacity benchmark
Sparsity	2-5% activation	Phase 1	K-WTA profiling
Reflex Latency	<100μs	Phase 2	Tile-level timing analysis
Multi-Tile Coherence	<1ms sync	Phase 3	Hub coordination profiler
Safety Gate Violations	0 per 1M queries	All	Witness log analysis
Circuit Breaker Rate	<0.1% of queries	All	Monitoring dashboard

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Integration with Cognitum Hardware

Cognitum v0 (Simulation)

Capabilities:

• Cycle-accurate simulation of tile architecture
• SRAM modeling with realistic latencies
• Event bus simulation with timing
• Power estimation models

Usage:

• Phase 1-2 development and validation
• Performance profiling before hardware availability
• Regression testing for deterministic timing

Limitations:

• No real power measurements (estimates only)
• Simulation overhead limits scale testing
• May miss hardware-specific edge cases

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Cognitum v1 (Hardware)

Capabilities:

• Physical neuromorphic tiles with on-tile SRAM
• Real power measurements (<1μJ per query target)
• Hardware-enforced deterministic timing
• Multi-chip interconnect for scaling

Usage:

• Phase 2-3 deployment and validation
• Real-world power and latency measurements
• Multi-chip scaling experiments
• Safety-critical deployment validation

Requirements:

• Tile firmware with reflex path implementation
• Hub software for coordination and consolidation
• Interconnect drivers for multi-chip communication
• Monitoring and instrumentation infrastructure

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Deployment Workflow

Development Workflow

1. Local Development

   • RuVector Server runs on developer workstation
   • Mock Cognitum simulator for reflex tier
   • Local Postgres for persistence
   • Unit tests + integration tests

2. Staging Environment

   • RuVector Server on dedicated server
   • Cognitum v0 simulator at scale
   • Staging Postgres with production-like data
   • Performance regression tests

3. Production Deployment

   • RuVector Server on high-memory server (128GB+)
   • Cognitum v1 hardware tiles
   • Production Postgres with replication
   • Full monitoring and alerting

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Deployment Checklist

Phase 1 (RuVector Foundation):

• HDC module passes all unit tests
• Hopfield capacity scaling validated
• K-WTA latency <100μs for d=10000
• 10× energy efficiency vs baseline HNSW
• Integration tests with ruvector-core pass
• Hybrid search demo functional

Phase 2 (Cognitum Reflex):

• Event bus handles 10kHz input stream
• Reflex latency <100μs (p99)
• BTSP one-shot learning accuracy >90%
• Zero off-tile memory access verified
• Witness logging functional
• Circuit breakers tested under load

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

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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

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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%

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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

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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)

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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/

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Document Version: 1.0 Last Updated: 2025-12-28 Maintainer: RuVector Nervous System Architecture Team