vrr/ruvector
2
0
Fork 0
mirror of https://github.com/ruvnet/RuVector.git synced 2026-05-29 19:33:34 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 1

docs: Update Micro HNSW README for version 2.2, correcting size and removing v2.3 features

Browse source
This commit is contained in:
rUv 2025-12-02 18:26:46 +00:00
parent b0721e40e4
commit f90604ee2a
2 changed files with 63 additions and 388 deletions
Download patch file Download diff file Expand all files Collapse all files

117
README.md
View file

@ -33,6 +33,31 @@ Traditional vector databases just store and search. When you ask "find similar i
Think of it as: **Pinecone + Neo4j + PyTorch + pgvector + etcd** in one Rust package.
## How the GNN Works
Traditional vector search:
```
Query → HNSW Index → Top K Results
```
RuVector with GNN:
```
Query → HNSW Index → GNN Layer → Enhanced Results
↑ │
└──── learns from ─────┘
```
The GNN layer:
1. Takes your query and its nearest neighborsa
2. Applies multi-head attention to weigh which neighbors matter
3. Updates representations based on graph structure
4. Returns better-ranked results
Over time, frequently-accessed paths get reinforced, making common queries faster and more accurate.
## Quick Start
### One-Line Install
@ -48,12 +73,32 @@ npm install ruvector
npx ruvector
```
## Comparison
| Feature | RuVector | Pinecone | Qdrant | Milvus | ChromaDB |
|---------|----------|----------|--------|--------|----------|
| **Latency (p50)** | **61µs** | ~2ms | ~1ms | ~5ms | ~50ms |
| **Memory (1M vec)** | 200MB* | 2GB | 1.5GB | 1GB | 3GB |
| **Graph Queries** | ✅ Cypher | ❌ | ❌ | ❌ | ❌ |
| **Hyperedges** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Self-Learning (GNN)** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **AI Agent Routing** | ✅ Tiny Dancer | ❌ | ❌ | ❌ | ❌ |
| **Raft Consensus** | ✅ | ❌ | ✅ | ❌ | ❌ |
| **Multi-Master Replication** | ✅ | ❌ | ❌ | ✅ | ❌ |
| **Auto-Compression** | ✅ 2-32x | ❌ | ❌ | ✅ | ❌ |
| **Browser/WASM** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Differentiable** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Open Source** | ✅ MIT | ❌ | ✅ | ✅ | ✅ |
*With PQ8 compression. Benchmarks on Apple M2 / Intel i7.
## Features
### Core Capabilities
Essential vector database features for storing, searching, and querying embeddings.
| Feature | What It Does | Why It Matters |
|---------|--------------|----------------|
| **Vector Search** | HNSW index, <0.5ms latency, SIMD acceleration | Fast enough for real-time apps |
@ -65,8 +110,6 @@ Essential vector database features for storing, searching, and querying embeddin
### Distributed Systems
Scale horizontally with production-grade clustering and replication.
| Feature | What It Does | Why It Matters |
|---------|--------------|----------------|
| **Raft Consensus** | Leader election, log replication | Strong consistency for metadata |
@ -81,8 +124,6 @@ cargo add ruvector-raft ruvector-cluster ruvector-replication
### AI & ML
Built-in machine learning capabilities for compression, routing, and trainable search.
| Feature | What It Does | Why It Matters |
|---------|--------------|----------------|
| **Tensor Compression** | f32→f16→PQ8→PQ4→Binary | 2-32x memory reduction |
@ -173,26 +214,8 @@ npx ruvector attention compute -t dot -d 128 # Run attention computation
npx ruvector attention hyperbolic -a distance -v "[0.1,0.2]" -b "[0.3,0.4]"
```
```javascript
// JavaScript API
const { FlashAttention, HyperbolicAttention, poincareDistance } = require('@ruvector/attention');
// Flash attention for long sequences
const flash = new FlashAttention(512, 64); // dim=512, block_size=64
const output = flash.compute(query, keys, values);
// Hyperbolic attention for hierarchical data
const hyper = new HyperbolicAttention(256, 1.0); // dim=256, curvature=1.0
const result = hyper.compute(query, keys, values);
// Hyperbolic distance
const dist = poincareDistance(new Float32Array([0.1, 0.2]), new Float32Array([0.3, 0.4]), 1.0);
```
### Deployment
Run anywhere—server, browser, or embedded in your application.
| Feature | What It Does | Why It Matters |
|---------|--------------|----------------|
| **HTTP/gRPC Server** | REST API, streaming support | Easy integration |
@ -230,52 +253,6 @@ Production-validated metrics at hyperscale:
| **Index Build Time** | 1M vectors/min | Parallel HNSW construction |
| **Replication Lag** | <100ms | Multi-master async replication |
## Comparison
| Feature | RuVector | Pinecone | Qdrant | Milvus | ChromaDB |
|---------|----------|----------|--------|--------|----------|
| **Latency (p50)** | **61µs** | ~2ms | ~1ms | ~5ms | ~50ms |
| **Memory (1M vec)** | 200MB* | 2GB | 1.5GB | 1GB | 3GB |
| **Graph Queries** | ✅ Cypher | ❌ | ❌ | ❌ | ❌ |
| **Hyperedges** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Self-Learning (GNN)** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **AI Agent Routing** | ✅ Tiny Dancer | ❌ | ❌ | ❌ | ❌ |
| **Attention Mechanisms** | ✅ 39 types | ❌ | ❌ | ❌ | ❌ |
| **Hyperbolic Embeddings** | ✅ Poincaré | ❌ | ❌ | ❌ | ❌ |
| **PostgreSQL Extension** | ✅ pgvector-compatible | ❌ | ❌ | ❌ | ❌ |
| **SIMD Optimization** | ✅ AVX-512/NEON | Partial | ✅ | ✅ | ❌ |
| **Metadata Filtering** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **Sparse Vectors** | ✅ BM25/TF-IDF | ✅ | ✅ | ✅ | ❌ |
| **Raft Consensus** | ✅ | ❌ | ✅ | ❌ | ❌ |
| **Multi-Master Replication** | ✅ | ❌ | ❌ | ✅ | ❌ |
| **Auto-Compression** | ✅ 2-32x | ❌ | ❌ | ✅ | ❌ |
| **Browser/WASM** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Differentiable** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Open Source** | ✅ MIT | ❌ | ✅ | ✅ | ✅ |
*With PQ8 compression. Benchmarks on Apple M2 / Intel i7.
## How the GNN Works
Traditional vector search:
```
Query → HNSW Index → Top K Results
```
RuVector with GNN:
```
Query → HNSW Index → GNN Layer → Enhanced Results
↑ │
└──── learns from ─────┘
```
The GNN layer:
1. Takes your query and its nearest neighbors
2. Applies multi-head attention to weigh which neighbors matter
3. Updates representations based on graph structure
4. Returns better-ranked results
Over time, frequently-accessed paths get reinforced, making common queries faster and more accurate.
## Compression Tiers

334
crates/micro-hnsw-wasm/README.md
View file

@ -1,41 +1,9 @@
# Micro HNSW v2.3 - Neuromorphic Vector Search Engine
# Micro HNSW v2.2 - Neuromorphic Vector Search Engine
[![Crates.io](https://img.shields.io/crates/v/micro-hnsw-wasm.svg)](https://crates.io/crates/micro-hnsw-wasm)
[![Documentation](https://docs.rs/micro-hnsw-wasm/badge.svg)](https://docs.rs/micro-hnsw-wasm)
[![License](https://img.shields.io/crates/l/micro-hnsw-wasm.svg)](https://github.com/ruvnet/ruvector/blob/main/LICENSE)
[![WASM Size](https://img.shields.io/badge/wasm-11.8KB-brightgreen.svg)](https://github.com/ruvnet/ruvector)
[![GitHub Stars](https://img.shields.io/github/stars/ruvnet/ruvector?style=social)](https://github.com/ruvnet/ruvector)
**[GitHub](https://github.com/ruvnet/ruvector)** | **[Documentation](https://docs.rs/micro-hnsw-wasm)** | **[ruv.io](https://ruv.io)** | **[Crates.io](https://crates.io/crates/micro-hnsw-wasm)**
---
A **11.8KB** neuromorphic computing core that fuses graph-based vector search (HNSW) with biologically-inspired spiking neural networks. Designed for 256-core ASIC deployment, edge AI, and real-time similarity-driven neural processing.
A **7.2KB** neuromorphic computing core that fuses graph-based vector search (HNSW) with biologically-inspired spiking neural networks. Designed for 256-core ASIC deployment, edge AI, and real-time similarity-driven neural processing.
> **Vector search meets brain-inspired computing** — query vectors trigger neural spikes, enabling attention mechanisms, winner-take-all selection, and online learning through spike-timing dependent plasticity (STDP).
## Key Features
- 🧠 **Neuromorphic Computing** - Spiking neural networks with LIF neurons, STDP learning
- 🔍 **HNSW Vector Search** - Fast approximate nearest neighbor search
- ⚡ **11.8KB WASM** - Ultra-minimal footprint for edge deployment
- 🎯 **58 Exported Functions** - Complete neuromorphic API
- 🔧 **No Dependencies** - Pure `no_std` Rust, zero allocations
- 🚀 **ASIC Ready** - Designed for 256-core custom silicon
## Novel Neuromorphic Discoveries (v2.3)
This release introduces groundbreaking neuromorphic computing features:
| Discovery | Description | Application |
|-----------|-------------|-------------|
| **Spike-Timing Vector Encoding** | Convert vectors to temporal spike patterns using first-spike coding | Energy-efficient similarity matching |
| **Homeostatic Plasticity** | Self-stabilizing network that maintains target activity levels | Robust long-running systems |
| **Oscillatory Resonance** | Gamma-rhythm (40Hz) synchronization for phase-based search | Attention and binding |
| **Winner-Take-All Circuits** | Competitive selection via lateral inhibition | Hard decision making |
| **Dendritic Computation** | Nonlinear local processing in dendritic compartments | Coincidence detection |
| **Temporal Pattern Recognition** | Spike history matching using Hamming similarity | Sequence learning |
## Why Micro HNSW + SNN?
Traditional vector databases return ranked results. Micro HNSW v2.2 goes further: similarity scores become neural currents that drive a spiking network. This enables:
@ -73,6 +41,18 @@ Traditional vector databases return ranked results. Micro HNSW v2.2 goes further
- **ASIC-ready**: Synthesizable for custom silicon
- **Edge-native**: Microcontrollers to data centers
"Real-World Applications" Section
| Application | Description |
|-----------------------------------|--------------------------------------------------------------------------------|
| 1. Embedded Vector Database | Semantic search on microcontrollers/IoT with 256-core sharding |
| 2. Knowledge Graphs | Cypher-style typed entities (GENE, PROTEIN, DISEASE) with spreading activation |
| 3. Self-Learning Systems | Anomaly detection that learns via STDP without retraining |
| 4. DNA/Protein Analysis | k-mer embeddings for genomic similarity with winner-take-all alignment |
| 5. Algorithmic Trading | Microsecond pattern matching with neural winner-take-all signals |
| 6. Industrial Control (PLC/SCADA) | Predictive maintenance via vibration analysis at the edge |
| 7. Robotics & Sensor Fusion | Multi-modal LIDAR/camera/IMU fusion with spike-based binding |
## Specifications
| Parameter | Value | Notes |
@ -84,7 +64,7 @@ Traditional vector databases return ranked results. Micro HNSW v2.2 goes further
| Beam Width | 3 | Search beam size |
| Node Types | 16 | 4-bit packed |
| SNN Neurons | 32 | One per vector |
| **WASM Size** | **~11.8KB** | After wasm-opt -Oz |
| **WASM Size** | **~7.2KB** | After wasm-opt -Oz |
| Gate Count | ~45K | Estimated for ASIC |
## Building
@ -207,99 +187,6 @@ const spikes = wasm.snn_get_spikes();
console.log(`Similar vectors that spiked: 0b${spikes.toString(2)}`);
```
### Novel Neuromorphic Features (v2.3)
```javascript
// ========== SPIKE-TIMING VECTOR ENCODING ==========
// Convert vectors to temporal spike patterns (first-spike coding)
const pattern0 = wasm.encode_vector_to_spikes(0);
const pattern1 = wasm.encode_vector_to_spikes(1);
// Compare patterns using Jaccard-like spike timing similarity
const similarity = wasm.spike_timing_similarity(pattern0, pattern1);
console.log(`Temporal similarity: ${similarity.toFixed(3)}`);
// Search using spike patterns instead of distance
const queryPattern = 0b10101010101010101010101010101010;
const found = wasm.spike_search(queryPattern, 5);
// ========== HOMEOSTATIC PLASTICITY ==========
// Self-stabilizing network maintains target activity (0.1 spikes/ms)
for (let i = 0; i < 1000; i++) {
wasm.snn_step(1.0); // 1ms timestep
wasm.homeostatic_update(1.0); // Adjust thresholds
}
console.log(`Spike rate neuron 0: ${wasm.get_spike_rate(0).toFixed(4)} spikes/ms`);
// ========== OSCILLATORY RESONANCE (40Hz GAMMA) ==========
// Phase-synchronized search for attention mechanisms
wasm.oscillator_step(1.0); // Advance oscillator phase
const phase = wasm.oscillator_get_phase();
console.log(`Oscillator phase: ${phase.toFixed(2)} radians`);
// Compute resonance (phase alignment) for each neuron
const resonance = wasm.compute_resonance(0);
console.log(`Neuron 0 resonance: ${resonance.toFixed(3)}`);
// Search with phase modulation (results boosted by resonance)
const phaseResults = wasm.resonance_search(5, 0.5); // k=5, weight=0.5
// ========== WINNER-TAKE-ALL CIRCUITS ==========
// Hard decision: only strongest neuron survives
const winner = wasm.wta_compete();
if (winner !== 255) {
console.log(`Winner: neuron ${winner}`);
}
// Soft competition (softmax-like proportional inhibition)
wasm.wta_soft();
// ========== DENDRITIC COMPUTATION ==========
// Nonlinear local processing in dendritic branches
wasm.dendrite_reset();
// Inject current to specific dendritic branch
wasm.dendrite_inject(0, 0, 1.5); // Neuron 0, branch 0, current 1.5
wasm.dendrite_inject(0, 1, 1.2); // Neuron 0, branch 1, current 1.2
// Nonlinear integration (coincident inputs get amplified)
const totalCurrent = wasm.dendrite_integrate(0);
console.log(`Dendritic current to soma: ${totalCurrent.toFixed(3)}`);
// Propagate spikes through dendritic tree (not just soma)
wasm.snn_step(1.0);
wasm.dendrite_propagate(0.5); // gain=0.5
// ========== TEMPORAL PATTERN RECOGNITION ==========
// Record spike history as shift register
for (let t = 0; t < 32; t++) {
wasm.snn_step(1.0);
wasm.pattern_record(); // Shift spikes into buffer
}
// Get spike pattern (32 timesteps encoded as bits)
const pattern = wasm.get_pattern(0);
console.log(`Neuron 0 spike history: 0b${pattern.toString(2).padStart(32, '0')}`);
// Find neuron with most similar spike history
const matchedNeuron = wasm.pattern_match(pattern);
console.log(`Best pattern match: neuron ${matchedNeuron}`);
// Find all neurons with correlated activity (Hamming distance ≤ 8)
const correlated = wasm.pattern_correlate(0, 8);
console.log(`Correlated neurons: 0b${correlated.toString(2)}`);
// ========== FULL NEUROMORPHIC SEARCH ==========
// Combined pipeline: HNSW + SNN + oscillation + WTA + patterns
queryBuf.set([0.9, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
const neuroResults = wasm.neuromorphic_search(5, 1.0, 20); // k=5, dt=1ms, 20 iterations
console.log(`Found ${neuroResults} matches via neuromorphic search`);
// Monitor network activity
const activity = wasm.get_network_activity();
console.log(`Network activity: ${activity.toFixed(3)} total spike rate`);
```
### GNN Message Passing
```javascript
@ -396,44 +283,6 @@ uint8_t snn_tick(float dt, float gain, uint8_t learn); // Combined step
float snn_get_time(void); // Get simulation time
uint8_t hnsw_to_snn(uint8_t k, float gain); // Search → neural activation
// ========== NOVEL NEUROMORPHIC API (NEW in v2.3) ==========
// Spike-Timing Vector Encoding
uint32_t encode_vector_to_spikes(uint8_t idx); // Vector → temporal spike pattern
float spike_timing_similarity(uint32_t a, uint32_t b); // Jaccard spike similarity
uint8_t spike_search(uint32_t query_pattern, uint8_t k); // Temporal code search
// Homeostatic Plasticity
void homeostatic_update(float dt); // Adjust thresholds for target rate
float get_spike_rate(uint8_t idx); // Running average spike rate
// Oscillatory Resonance
void oscillator_step(float dt); // Update gamma oscillator phase
float oscillator_get_phase(void); // Current phase (0 to 2π)
float compute_resonance(uint8_t idx); // Phase alignment score
uint8_t resonance_search(uint8_t k, float weight); // Phase-modulated search
// Winner-Take-All Circuits
void wta_reset(void); // Reset WTA state
uint8_t wta_compete(void); // Hard WTA, returns winner
void wta_soft(void); // Soft competition (softmax-like)
// Dendritic Computation
void dendrite_reset(void); // Clear dendritic compartments
void dendrite_inject(uint8_t n, uint8_t b, float i); // Inject to branch
float dendrite_integrate(uint8_t neuron); // Nonlinear integration
void dendrite_propagate(float gain); // Spike to dendrite routing
// Temporal Pattern Recognition
void pattern_record(void); // Shift current spikes into buffer
uint32_t get_pattern(uint8_t idx); // Get spike history (32 timesteps)
uint8_t pattern_match(uint32_t target); // Find best matching neuron
uint32_t pattern_correlate(uint8_t idx, uint8_t thresh); // Find correlated neurons
// Combined Neuromorphic Search
uint8_t neuromorphic_search(uint8_t k, float dt, uint8_t iters); // Full pipeline
float get_network_activity(void); // Total spike rate across network
// SearchResult structure (8 bytes)
typedef struct {
uint8_t idx;
@ -902,111 +751,6 @@ The `verilog/` directory contains synthesizable RTL for direct ASIC implementati
└─────────────────────────────────────────────────────────────┘
```
## ASIC Synthesis Guidelines (v2.3)
### Novel Hardware Blocks
The v2.3 neuromorphic features map to dedicated hardware units:
```
┌──────────────────────────────────────────────────────────────────────────┐
│ NEUROMORPHIC ASIC ARCHITECTURE │
├──────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ SPIKE ENCODER │ │ GAMMA OSCILLATOR│ │ WTA CIRCUIT │ │
│ │ Vector→Spikes │ │ 40Hz Phase Gen │ │ Lateral Inhib │ │
│ │ 8-bit temporal │ │ sin/cos LUT │ │ Max detector │ │
│ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ │
│ │ │ │ │
│ └────────────────────┼────────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ DENDRITIC TREE PROCESSOR │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │
│ │ │ Branch 0 │ │ Branch 1 │ │ Branch 2 │ │ Branch 3 │ ... ×6 │ │
│ │ │ σ nonlin │ │ σ nonlin │ │ σ nonlin │ │ σ nonlin │ │ │
│ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │
│ │ └────────────┼────────────┼────────────┘ │ │
│ │ ▼ ▼ │ │
│ │ ┌─────────────────────┐ │ │
│ │ │ SOMA INTEGRATOR │ │ │
│ │ └─────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ HOMEOSTATIC CONTROLLER │ │
│ │ Target rate: 0.1 spikes/ms | Threshold adaptation: τ=1000ms │ │
│ │ Sliding average spike counter → PID threshold adjustment │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ PATTERN RECOGNITION UNIT │ │
│ │ 32-bit shift registers × 32 neurons = 128 bytes │ │
│ │ Hamming distance comparator (parallel XOR + popcount) │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────────────┘
```
### Synthesis Estimates (v2.3)
| Block | Gate Count | Area (μm²) | Power (mW) | Notes |
|-------|------------|------------|------------|-------|
| Spike Encoder | ~2K | 800 | 0.02 | Vector→temporal conversion |
| Gamma Oscillator | ~500 | 200 | 0.01 | Phase accumulator + LUT |
| WTA Circuit | ~1K | 400 | 0.05 | Parallel max + inhibit |
| Dendritic Tree (×32) | ~8K | 3200 | 0.4 | Nonlinear branches |
| Homeostatic Ctrl | ~1.5K | 600 | 0.03 | PID + moving average |
| Pattern Unit | ~3K | 1200 | 0.1 | 32×32 shift + Hamming |
| **v2.3 Total** | **~60K** | 24,000 | 1.0 | Full neuromorphic |
| **v2.2 Baseline** | ~45K | 18,000 | 0.7 | SNN + HNSW only |
### Clock Domains
1. **Core Clock (500 MHz)**: HNSW search, distance calculations
2. **SNN Clock (1 kHz)**: Biological timescale for membrane dynamics
3. **Oscillator Clock (40 Hz)**: Gamma rhythm for synchronization
4. **Homeostatic Clock (1 Hz)**: Slow adaptation for stability
### Verilog Module Hierarchy
```verilog
module neuromorphic_hnsw (
input clk_core, // 500 MHz
input clk_snn, // 1 kHz
input clk_gamma, // 40 Hz
input rst_n,
// AXI-Lite interface
input [31:0] axi_addr,
input [31:0] axi_wdata,
output [31:0] axi_rdata,
// Spike I/O
output [31:0] spike_out,
input [31:0] spike_in
);
// Core instances
hnsw_core #(.CORE_ID(i)) cores[255:0] (...);
// Neuromorphic additions (v2.3)
spike_encoder enc (.clk(clk_core), ...);
gamma_oscillator osc (.clk(clk_gamma), ...);
wta_circuit wta (.clk(clk_core), ...);
dendritic_tree dend[31:0] (.clk(clk_snn), ...);
homeostatic_ctrl homeo (.clk(clk_snn), ...);
pattern_recognizer pat (.clk(clk_core), ...);
result_merger merge (...);
endmodule
```
### FPGA Implementation Notes
For Xilinx Zynq-7000 / Artix-7:
- **Resource usage**: ~60% LUTs, ~40% FFs, ~30% BRAMs
- **Fmax**: 450 MHz (core clock meets timing easily)
- **Power**: ~800mW dynamic
- **Latency**: 2.5μs for 8K-vector neuromorphic search
## Version History
| Version | Size | Features |
@ -1014,8 +758,7 @@ For Xilinx Zynq-7000 / Artix-7:
| v1 | 4.6KB | L2 only, single core, greedy search |
| v2 | 7.3KB | +3 metrics, +multi-core, +beam search |
| v2.1 | 5.5KB | +node types, +edge weights, +GNN updates, wasm-opt |
| v2.2 | 7.2KB | +LIF neurons, +STDP learning, +spike propagation, +HNSW-SNN bridge |
| **v2.3** | **15KB** | +Spike-timing encoding, +Homeostatic plasticity, +Oscillatory resonance, +WTA circuits, +Dendritic computation, +Temporal pattern recognition, +Neuromorphic search pipeline |
| **v2.2** | **7.2KB** | +LIF neurons, +STDP learning, +spike propagation, +HNSW-SNN bridge |
## Performance
@ -1032,7 +775,6 @@ For Xilinx Zynq-7000 / Artix-7:
## SNN Parameters (Compile-time)
### Core SNN Parameters
| Parameter | Value | Description |
|-----------|-------|-------------|
| TAU_MEMBRANE | 20.0 | Membrane time constant (ms) |
@ -1043,50 +785,6 @@ For Xilinx Zynq-7000 / Artix-7:
| STDP_A_MINUS | 0.012 | LTD magnitude |
| TAU_STDP | 20.0 | STDP time constant (ms) |
### Novel Neuromorphic Parameters (v2.3)
| Parameter | Value | Description |
|-----------|-------|-------------|
| HOMEOSTATIC_TARGET | 0.1 | Target spike rate (spikes/ms) |
| HOMEOSTATIC_TAU | 1000.0 | Homeostasis time constant (slow) |
| OSCILLATOR_FREQ | 40.0 | Gamma oscillation frequency (Hz) |
| WTA_INHIBITION | 0.8 | Winner-take-all lateral inhibition |
| DENDRITIC_NONLIN | 2.0 | Dendritic nonlinearity exponent |
| SPIKE_ENCODING_RES | 8 | Temporal encoding resolution (bits) |
## Contributing
Contributions are welcome! Please see our [Contributing Guide](https://github.com/ruvnet/ruvector/blob/main/CONTRIBUTING.md) for details.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## Community & Support
- **GitHub Issues**: [Report bugs or request features](https://github.com/ruvnet/ruvector/issues)
- **Discussions**: [Join the conversation](https://github.com/ruvnet/ruvector/discussions)
- **Website**: [ruv.io](https://ruv.io)
## Citation
If you use Micro HNSW in your research, please cite:
```bibtex
@software{micro_hnsw_wasm,
title = {Micro HNSW: Neuromorphic Vector Search Engine},
author = {rUv},
year = {2024},
url = {https://github.com/ruvnet/ruvector},
version = {2.3.0}
}
```
## License
MIT OR Apache-2.0
---
**Built with ❤️ by [rUv](https://ruv.io)** | **[GitHub](https://github.com/ruvnet/ruvector)** | **[Crates.io](https://crates.io/crates/micro-hnsw-wasm)**