Performance-portable code is hard to produce due to diversity and heterogeneity of the state-of-the-art hardware platforms. Even more complex is the task of optimizing Artificial Neural Networks (ANNs) towards multiple hardware platforms. Manual optimization is expensive, while modern automated tools either support a narrow set of platforms or do not exploit individual strengths of different platforms to the fullest.

The functional data-parallel language Lift was shown to be performance-portable; the performance of the compiled OpenCL code is on par or better than that of highly tuned platform-specific libraries. This project aims to extend the method to the domain of Artificial Neural Networks by integrating domain-specific optimisations into the rewrite rules-based Lift compiler.

Optimizational methods of interest

  • Parallel mappings space exploration
  • Memory tiling
  • Memory coalescing
  • Approximate computations
  • Float quantization
  • Neuron pruning
  • Training batch size autotuning
  • Varying precision across layers and neurons
  • Convolution kernel decomposition
  • Sharing 32-bit registers
  • OpenCL kernel fusion
  • Expression simplification
  • Proprietary instruction sets usage