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UID:pretalx-coscup-2026-SVZZQG@pretalx.coscup.org
DTSTART;TZID=CST:20260808T103000
DTEND;TZID=CST:20260808T110000
DESCRIPTION:Large Language Models may start in frameworks like PyTorch\, bu
 t getting them to run efficiently on CPUs\, GPUs\, and specialized acceler
 ators increasingly depends on compiler infrastructure.\n\nAs machine learn
 ing systems have grown more complex and hardware has become more specializ
 ed\, the compiler layer has become a key bridge between model code and mac
 hine execution.\n\nThis talk introduces AI compilers from a practical pers
 pective\, covering why they matter\, what kinds of problems they solve\, a
 nd how open-source projects such as LLM MLIR and OpenXLA/XLA fit into the 
 broader stack. \nIt will also introduce a bit about tinygrad\, which is tr
 ying to take another approach rather than LLVM\, to compiler models runnin
 g on the hardware..\n\nIt also explains how ideas such as intermediate rep
 resentations\, lowering\, and backend targeting connect frameworks like Py
 Torch to real hardware.\n\nA small demo based on PyTorch and various compi
 lers (e.g. LLVM and tinygrad) helps make the overall pipeline more concret
 e and easier to understand.
DTSTAMP:20260713T142815Z
LOCATION:TR212
SUMMARY:From PyTorch to Hardware: An Introduction to LLM Compilers - Tommy 
 Han
URL:https://pretalx.coscup.org/coscup-2026/talk/SVZZQG/
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