A high-performance OpenCL implementation of neural network primitives achieving 27x speedup on matrix operations over CPU baselines.
This project demonstrates GPU-accelerated neural network computation through optimized OpenCL kernels. The system implements core mathematical operations including matrix multiplication, addition, transpose, and activation functions (ReLU, Sigmoid, Tanh) with comprehensive performance benchmarking.
The neural network follows a simple feedforward architecture: Input Layer (3) → Hidden Layer (4) → Output Layer (2). Matrix operations use optimized OpenCL kernels with local memory tiling, while activation functions leverage vectorized GPU implementations for maximum performance.
The system achieves significant speedups across different operations. Matrix multiplication shows the most dramatic improvement, with 512×512 matrices running 27.34x faster on GPU than CPU (105.49ms vs 3.86ms). Smaller matrices like 256×256 achieve 9.33x speedup, while activation functions on 1M-element vectors show 2.27x improvement.
| Operation | Matrix Size | CPU (ms) | GPU (ms) | Speedup |
|---|---|---|---|---|
| Matrix Mult | 512×512 | 105.49 | 3.86 | 22.23x |
| Matrix Mult | 256×256 | 11.61 | 1.26 | 9.33x |
| Activation | 1M elements | 2.92 | 1.29 | 2.27x |
Built with C++17 and OpenCL 1.2, the system automatically detects available hardware and compiles kernels for optimal performance. The CMake build system ensures cross-platform compatibility across macOS, Linux, and Windows. Memory management is handled efficiently through GPU buffer allocation and optimized data transfer patterns.
mkdir build && cd build
cmake .. && make -j4
./OpenCL_Neural_NetworkThis project showcases GPU programming expertise through OpenCL kernel development and optimization. It demonstrates understanding of neural network mathematical foundations, performance benchmarking techniques, and production-ready software engineering practices. The modular architecture provides a solid foundation for extending to training pipelines and deep learning applications.
What This Project Currently Does:
- Matrix Operations: GPU-accelerated multiplication, addition, and transpose operations
- Activation Functions: ReLU, Sigmoid, and Tanh with GPU-accelerated derivatives
- Neural Network Demo: A simple 3-layer feedforward network that performs forward pass computation
- Performance Benchmarking: CPU vs GPU performance comparison with detailed timing analysis
What This Project Does NOT Do (Yet):
- Training: No backpropagation or weight updates implemented
- Learning: Cannot learn from data or improve performance over time
- Real Inference: The demo network uses random weights, not trained parameters
- Deep Learning: No convolutional layers, pooling, or complex architectures
Current Status: This is Stage 1 of a planned 3-stage development roadmap. The current implementation provides the mathematical foundation and GPU acceleration infrastructure needed for neural network computation, but is not yet a complete neural network training or inference system.
Planned enhancements include implementing stochastic gradient descent for training, adding convolutional layers for image processing, and developing end-to-end neural network training capabilities.
Built with OpenCL, C++17, and GPU optimization techniques.