Artificial intelligence kernel generation targets performance-critical programs that execute mathematical operations on graphics processors or other accelerators. An agent can explore implementations, memory layouts, parallelization strategies, and hardware-specific instructions while measuring correctness and speed.
Generated kernels need deterministic tests and representative benchmarks. A speedup can be misleading if it changes numerical behavior, works only for narrow inputs, excludes compilation cost unfairly, or depends on hardware behavior that is not portable.
