🧠 ANN from Scratch in C (CPU → GPU)

🚧 Phase 1: CPU Implementation

• 🔧 Build System – Set up Makefile or CMake
• 🧱 Data Structures – Define Matrix, Layer, Network
• ➗ Matrix Operations – Add, multiply, transpose
• 🔁 Activation Functions – Sigmoid, ReLU, Softmax
• 🔄 Forward Propagation – Weighted sums + activations
• 📉 Loss Function – MSE or Cross-Entropy
• 🧮 Backpropagation – Calculate gradients
• 🏋️ Weight Updates – Apply SGD
• 🌀 Training Loop – Run for N epochs
• ✅ Test Cases (CPU)
  • Matrix operations match expected results
  • Activation outputs correct for sample inputs
  • Forward propagation output sanity check
  • Backpropagation gradients validated numerically
  • Training on XOR dataset: loss decreases, correct outputs

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🚀 Phase 2: GPU Acceleration (CUDA)

• ⚙️ CUDA Setup – Configure build and test kernel
• 🧊 Matrix Ops (GPU) – Port add, multiply, etc.
• 🌐 Activations (GPU) – Parallel element-wise ops
• 🧬 Forward Prop (GPU) – Matrix ops + activations
• 🔙 Backprop (GPU) – Gradient calculation on GPU
• 🏁 Training Loop (GPU) – Fully GPU-accelerated
• 🔍 Test Cases (GPU)
  • GPU matrix ops produce identical results to CPU
  • Activation functions match CPU outputs
  • Forward propagation matches CPU outputs
  • Backpropagation gradients match CPU calculations
  • Training on XOR dataset converges with GPU

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