Temporal Neural Solver

Ultra-fast neural network inference engine achieving sub-microsecond latency through mathematical optimization and hardware acceleration.

🚀 Performance

Benchmarked on x86_64 with AVX2 (100,000 iterations):

Features

• 🎯 Sub-microsecond inference: <1µs P99.9 latency for 128→32→4 networks
• ⚡ Hardware acceleration: AVX2/AVX-512 SIMD, INT8 quantization
• 🔬 Mathematical optimization: Kalman filtering, sublinear solvers
• 📊 Comprehensive validation: Statistical significance testing included
• 🦀 Pure Rust: Safe, fast, and zero dependencies on ML frameworks
• 📦 Easy integration: Simple API, npm package available

Installation

Rust

[dependencies]
temporal-neural-solver = "1.0"

Node.js

npm install temporal-neural-solver

Quick Start

Rust

use temporal_neural_solver::optimizations::optimized::UltraFastTemporalSolver;

fn main() {
    // Create solver
    let mut solver = UltraFastTemporalSolver::new();

    // Prepare input (128 dimensions)
    let input = [0.1f32; 128];

    // Run inference
    let (output, duration) = solver.predict_optimized(&input);

    println!("Output: {:?}", output);  // 4-dimensional output
    println!("Latency: {:?}", duration); // ~500ns
}

Node.js

const { TemporalSolver } = require('temporal-neural-solver');

const solver = new TemporalSolver();
const input = new Float32Array(128).fill(0.1);

solver.predict(input).then(output => {
    console.log('Output:', output);
    console.log('Latency:', solver.lastLatencyNs, 'ns');
});

CLI

# Install CLI
cargo install temporal-neural-solver

# Run prediction
temporal-solver predict --input "0.1,0.2,0.3,..."

# Run benchmark
temporal-solver benchmark --iterations 10000

# Check system info
temporal-solver info

Architecture

The solver implements a 3-layer neural network (128→32→4) with:

1. Neural Network: Optimized matrix operations with ReLU activation
2. Temporal Filtering: Kalman filter for temporal coherence
3. Mathematical Verification: Sublinear solver with error certificates
4. Hardware Optimization: Platform-specific SIMD acceleration

Module Structure

temporal-neural-solver/
├── baselines/        # Traditional implementations for comparison
├── optimizations/    # Optimized implementations (AVX2, INT8)
├── solvers/         # Mathematical solver integration
├── benchmarks/      # Comprehensive validation framework
└── core/           # Core types and utilities

Validation

This crate includes extensive validation to prove performance claims:

# Run simple proof (quick validation)
cargo run --release --bin simple_proof

# Run comprehensive comparison
cargo run --release --bin prove_performance

# Run full validation suite
./validate.sh

Benchmark Results

Comparison with traditional implementations (10,000 iterations):

How It Works

1. Memory Optimization

• Cache-aligned memory allocation (32-byte boundaries)
• Pre-allocated buffers (zero runtime allocations)
• Optimal data layout for SIMD

2. Computation Optimization

• AVX2 SIMD instructions (8x parallelism)
• INT8 quantization where applicable
• Loop unrolling in critical paths
• Branchless operations

3. Mathematical Optimization

• Kalman filtering for temporal coherence
• Neumann series solver for verification
• Reduced computational complexity

Building from Source

# Clone repository
git clone https://github.com/yourusername/temporal-neural-solver
cd temporal-neural-solver

# Build with optimizations
RUSTFLAGS="-C target-cpu=native -C target-feature=+avx2" cargo build --release

# Run tests
cargo test --release

# Run benchmarks
cargo bench

Requirements

• CPU: x86_64 with AVX2 support (AVX-512 optional)
• RAM: <1MB (all pre-allocated)
• OS: Linux, macOS, Windows
• Rust: 1.70+

Examples

See the examples/ directory for:

• Basic inference
• Batch processing
• Real-time applications
• Integration with existing systems

Documentation

• API Documentation
• Performance Analysis
• Optimization Guide
• Validation Report

Contributing

Contributions are welcome! Please ensure:

• All tests pass (cargo test)
• Performance benchmarks meet targets
• Code follows Rust best practices

License

MIT License - see LICENSE for details.

Citation

@software{temporal_neural_solver,
  title = {Temporal Neural Solver: Sub-microsecond Neural Network Inference},
  author = {Sublinear Solver Team},
  year = {2024},
  url = {https://github.com/yourusername/temporal-neural-solver}
}

Acknowledgments

Built with Rust 🦀 for maximum performance and safety.

Note: Performance measurements taken on x86_64 Linux with Intel/AMD CPUs supporting AVX2. Results may vary based on hardware.