NeoCore™ - Next Generation CPU-Native Transformer

🚀 Overview

NeoCore is a state-of-the-art, CPU-optimized transformer architecture designed for edge computing and enterprise deployment. By leveraging advanced CPU-specific optimizations and modern architectural improvements, NeoCore achieves exceptional performance without requiring GPU acceleration.

Key Features

• 🔋 CPU-Native Design: Optimized from the ground up for modern CPU architectures
• 🎯 Memory Efficient: Advanced caching and chunking strategies for optimal memory usage
• 🛠 Enterprise Ready: Production-grade implementation with comprehensive logging and monitoring
• 🔄 Modern Architecture: Incorporates Multi-Query Attention, RMSNorm, and Rotary Embeddings
• 📊 Extensive Benchmarking: Built-in performance profiling and optimization tools

🔧 Installation

pip install neocore

🏗 Architecture

NeoCore introduces several architectural innovations:

Core Components

1. Multi-Query Attention (MQA)

Q: [Batch, Seq, Heads, Head_Dim]  # Multiple query heads
K,V: [Batch, 1, Head_Dim]         # Single key/value

2. RMSNorm for Stabilization

RMSNorm(x) = x * scale / sqrt(mean(x²) + ε)

3. Block-wise Computation

Input -> Chunked Processing -> Cache-Friendly Operations -> Output

Performance Optimizations

Memory Access Pattern

┌──────────────────┐
│ Input Embedding  │
└────────┬─────────┘
         │
    ┌────▼────┐
    │ Chunk 1 │──┐
    └─────────┘  │
    ┌─────────┐  │
    │ Chunk 2 │──┼─► Parallel Processing
    └─────────┘  │
    ┌─────────┐  │
    │ Chunk N │──┘
    └─────────┘

💫 Key Innovations

1. Cache-Optimized Linear Operations

• Custom blocked matrix multiplication
• Adaptive chunk sizing
• Operation result caching

2. Efficient Attention Mechanism

# Traditional vs NeoCore MQA
Traditional: O(N * H * D) memory
NeoCore:     O(N * D) memory

3. Advanced Position Encoding

• Rotary embeddings for enhanced position awareness
• Cache-friendly implementation
• Optimized for CPU SIMD operations

📊 Performance Metrics

Batch Size	Sequence Length	Processing Time (ms)	Tokens/Second
1	32	31.17	1,026
4	64	43.51	5,883
16	128	161.28	12,700

🚀 Quick Start

from neocore import NoamConfig, CPUOptimizedNoamTransformer

# Initialize configuration
config = NoamConfig(
    d_model=512,
    n_heads=8,
    n_layers=6,
    warmup_steps=4000,
    chunk_size=32
)

# Create model
model = CPUOptimizedNoamTransformer(config)

# Process input
output = model(input_ids)

🎯 Use Cases

• Edge Computing: Optimal for deployment on CPU-only edge devices
• Enterprise Systems: Reliable performance on standard server hardware
• CI/CD Pipelines: Efficient inference in production pipelines
• Privacy-First Applications: On-device processing without GPU requirements

🔬 Technical Details

Memory Management

• Intelligent cache management system
• Adaptive chunk sizing based on input
• Memory-efficient attention patterns

Threading Model

Number of Threads = min(CPU_COUNT, MAX_EFFICIENT_THREADS)
Thread Pool Size = Adaptive based on workload

Optimization Levels

1. Level 1: Basic CPU optimizations
2. Level 2: Cache-aware operations
3. Level 3: Advanced parallelization
4. Level 4: Full SIMD utilization

📈 Benchmarking

Run comprehensive benchmarks:

python -m neocore.benchmark --config benchmark_config.yaml

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📜 License

Apache License 2.0. See LICENSE for details.

🌟 Acknowledgments

Built on modern transformer innovations with specific optimizations for CPU architectures. Special thanks to the research community for their groundbreaking work in efficient transformer designs.

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Citation

@software{neocore2024,
  title={NeoCore: CPU-Optimized Transformer Architecture},
  author={Kye Gomez},
  year={2024},
  publisher={GitHub},
  url={https://github.com/neocore/neocore}
}