Aurelia Language

Aurelia is a systems programming language designed for the AI-native era, featuring first-class tensor primitives and automatic differentiation built directly into the language.

Core Features

First-Class Tensor Primitives

// Tensor is a native type, not a library import
let tensor = Tensor<f32>[3, 3]

// Automatic differentiation
let grad = autodiff(tensor) { x => x * x + 2 * x + 1 }

// Neural network primitives
let layer = Linear(input=784, output=256)
let activation = ReLU(layer.output)

Type System

Aurelia's type system bridges static and dynamic:

Memory Management

• Ownership Model: Zero-cost borrow checking
• Region-based Allocation: Predictable memory patterns
• NPU-aware: Direct memory mapping to neural processing units

Example: Neural Network

module MNISTClassifier {
    struct Model {
        conv1: Conv2D(1, 32, kernel=3)
        conv2: Conv2D(32, 64, kernel=3)
        fc1: Linear(64*7*7, 128)
        fc2: Linear(128, 10)
    }
    
    fn forward(self, x: Tensor<f32>[28, 28]) -> Tensor<f32>[10] {
        x |> self.conv1 |> ReLU |> MaxPool(2)
          |> self.conv2 |> ReLU |> MaxPool(2)
          |> Flatten
          |> self.fc1 |> ReLU
          |> self.fc2 |> Softmax
    }
}

// Training
let model = MNISTClassifier.Model()
let optimizer = Adam(lr=0.001)

for epoch in 0..10 {
    for batch in dataset.batches(64) {
        let loss = CrossEntropy(model.forward(batch.x), batch.y)
        optimizer.step(loss.backward())
    }
}

Integration with Zenith Kernel

Aurelia code compiles directly to:

1. CPU Instructions: Standard LLVM backend
2. GPU Kernels: CUDA/ROCm via MLIR
3. NPU Bytecode: Direct neural processing unit targeting

Aurelia: Where systems meet intelligence.