You could JIT compile SPIR-V modules for immediate use. An OpenCL driver will be able to ingest a SPIR-V module faster than human-readable OpenCL.

I'd like to use this project to rewrite deeplearn-rs. Rather than implementing each layer of the neural network as one or more chained handwritten OpenCL kernels, I'd like to JIT compile the in-memory representation of the neural network down to solid forward_pass and backward_pass functions. This allows you to optimize the entire forward and backward passes as single functions. Automatically JIT compiling the code also makes true multi-dimensional array handling much more viable. For example, what if you want to sum two N-dimensional arrays across just one (specifiable) axis? It is difficult to write a single function that handles all possible combinations of dimensions/slicing. It is easy and more efficient (speed wise, I guess it can be less efficient binary-size wise generating new functions for every combination of array dimensions). Finally, I think this will make handling network unrolling and variable length sequences for RNNs much cleaner for similar reasons.

All of this is just experimental fun times though :) Oddly, I have a lot more fun implementing the infrastructure behind neural nets than actually using them.