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* docs: Add comprehensive GNN v2 implementation plans Add 22 detailed planning documents for 19 advanced GNN features: Tier 1 (Immediate - 3-6 months): - GNN-Guided HNSW Routing (+25% QPS) - Incremental Graph Learning/ATLAS (10-100x faster updates) - Neuro-Symbolic Query Execution (hybrid neural + logical) Tier 2 (Medium-Term - 6-12 months): - Hyperbolic Embeddings (Poincaré ball model) - Degree-Aware Adaptive Precision (2-4x memory reduction) - Continuous-Time Dynamic GNN (concept drift detection) Tier 3 (Research - 12+ months): - Graph Condensation (10-100x smaller graphs) - Native Sparse Attention (8-15x GPU speedup) - Quantum-Inspired Attention (long-range dependencies) Novel Innovations (10 experimental features): - Gravitational Embedding Fields, Causal Attention Networks - Topology-Aware Gradient Routing, Embedding Crystallization - Semantic Holography, Entangled Subspace Attention - Predictive Prefetch Attention, Morphological Attention - Adversarial Robustness Layer, Consensus Attention Includes comprehensive regression prevention strategy with: - Feature flag system for safe rollout - Performance baseline (186 tests + 6 search_v2 tests) - Automated rollback mechanisms Related to #38 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(micro-hnsw-wasm): Add neuromorphic HNSW v2.3 with SNN integration ## New Crate: micro-hnsw-wasm v2.3.0 - Published to crates.io: https://crates.io/crates/micro-hnsw-wasm - 11.8KB WASM binary with 58 exported functions - Neuromorphic vector search combining HNSW + Spiking Neural Networks ### Core Features - HNSW graph-based approximate nearest neighbor search - Multi-distance metrics: L2, Cosine, Dot product - GNN extensions: typed nodes, edge weights, neighbor aggregation - Multi-core sharding: 256 cores × 32 vectors = 8K total ### Spiking Neural Network (SNN) - LIF (Leaky Integrate-and-Fire) neurons with membrane dynamics - STDP (Spike-Timing Dependent Plasticity) learning - Spike propagation through graph topology - HNSW→SNN bridge for similarity-driven neural activation ### Novel Neuromorphic Features (v2.3) - Spike-Timing Vector Encoding (rate-to-time conversion) - Homeostatic Plasticity (self-stabilizing thresholds) - Oscillatory Resonance (40Hz gamma synchronization) - Winner-Take-All Circuits (competitive selection) - Dendritic Computation (nonlinear branch integration) - Temporal Pattern Recognition (spike history matching) - Combined Neuromorphic Search pipeline ### Performance Optimizations - 5.5x faster SNN tick (2,726ns → 499ns) - 18% faster STDP learning - Pre-computed reciprocal constants - Division elimination in hot paths ### Documentation & Organization - Reorganized docs into subdirectories (gnn/, implementation/, publishing/, status/) - Added comprehensive README with badges, SEO, citations - Added benchmark.js and test_wasm.js test suites - Added DEEP_REVIEW.md with performance analysis - Added Verilog RTL for ASIC synthesis 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
60 lines
1.3 KiB
TOML
60 lines
1.3 KiB
TOML
[package]
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name = "ruvector-cloudrun-gpu"
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version = "0.1.0"
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edition = "2021"
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description = "RuVector Cloud Run GPU benchmarks with self-learning models"
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license = "MIT"
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[[bin]]
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name = "gpu-benchmark"
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path = "src/main.rs"
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[dependencies]
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# RuVector core crates
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ruvector-core = { path = "../../crates/ruvector-core", default-features = false }
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ruvector-gnn = { path = "../../crates/ruvector-gnn" }
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ruvector-attention = { path = "../../crates/ruvector-attention" }
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ruvector-graph = { path = "../../crates/ruvector-graph", default-features = false, features = ["wasm"] }
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# Async runtime
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tokio = { version = "1.41", features = ["full"] }
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# CLI and output
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clap = { version = "4.5", features = ["derive"] }
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indicatif = "0.17"
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console = "0.15"
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# Serialization
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# HTTP server for Cloud Run
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axum = "0.7"
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tower = "0.4"
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tower-http = { version = "0.5", features = ["cors", "trace"] }
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# Metrics and timing
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hdrhistogram = "7.5"
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sysinfo = "0.31"
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chrono = "0.4"
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# Math and data
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rand = "0.8"
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rand_distr = "0.4"
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rayon = "1.10"
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# Error handling
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anyhow = "1.0"
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thiserror = "2.0"
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# Tracing
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
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[features]
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default = []
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[profile.release]
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opt-level = 3
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lto = "thin"
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codegen-units = 4
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