Tile, Fuse, Repeat: Why Layout Matters for AI Performance
Every time a neural network runs, there’s a silent negotiation between compute and memory. It’s naive to think ML compilers optimize just the compute - the FLOPs. In reality, they optimize movement. The most expensive operation in modern compute isn’t your matrix multiply; it’s getting data from memory to the compute units. This post explores how layout, tiling, and fusion are the unsung heroes of ML compiler performance. 1. The Compiler’s Hidden Battle Deep Learning performance is a balancing act between compute and memory. You can think of layout as the grammar of that relationship - the way tensors are arranged, accessed, and aligned. ...