AI-Native Systems for Portable Kernels Across Hardware Architectures¶
Modern AI kernels are overwhelmingly written for CUDA, making it difficult to bring state-of-the-art optimizations to emerging hardware backends. In this post, we show how IBM Research collaborated with the K-Search team to automatically translate CUDA kernels to MLX, demonstrating that evolutionary kernel optimization can dramatically reduce the engineering effort required to port high-performance kernels while maintaining competitive performance.
Originally published on the BAIR Blog
This post was originally published on the Berkeley AI Research (BAIR) blog.