architecture.md

Neural Network Architecture

This document outlines the architectural design of our neural network implementation in Zig.

Core Design Principles

1. Memory Safety: Utilizing Zig's memory safety features and explicit allocation
2. Performance: Optimized matrix operations and efficient memory usage
3. Modularity: Clear separation of concerns between components
4. Extensibility: Easy to add new layer types and activation functions

Component Overview

Matrix Operations (matrix.zig)

• Core mathematical operations
• Memory-efficient implementation
• Support for batch operations
• Optimized for neural network computations

Layers (layer.zig)

Two main layer types:

1. Standard Layer

   • Traditional fully connected layer
   • Configurable activation functions
   • Weight and bias management

2. Gated Layer

   • Support for GLU and SwiGLU
   • Dual weight matrices
   • Optimized gating operations

Network (network.zig)

• Flexible layer composition
• Dynamic architecture building
• Support for different loss functions:
  • Mean Squared Error
  • Cross Entropy
• Mini-batch training capabilities

Memory Management

Allocation Strategy

• Clear ownership model
• Explicit deallocation
• Efficient matrix reuse
• Temporary buffer management

Resource Lifecycle

1. Network initialization
2. Layer allocation
3. Forward propagation buffers
4. Backward propagation gradients
5. Clean deallocation

Error Handling

Using Zig's error union types for handling:

• Invalid dimensions
• Memory allocation failures
• Numerical instabilities
• Configuration errors

Performance Optimizations

1. Matrix Operations

   • Cache-friendly memory layout
   • Vectorized operations where possible
   • Minimal temporary allocations

2. Training Process

   • Efficient mini-batch processing
   • Smart gradient accumulation
   • Memory reuse between iterations

3. Memory Layout

   • Contiguous memory for matrices
   • Aligned allocations
   • Efficient striding

Future Considerations

Areas for potential improvement:

1. SIMD optimizations
2. GPU acceleration
3. Distributed training support
4. Additional layer types:
   • Convolutional layers
   • Attention mechanisms
   • Residual connections