  This paper discusses our proposal and implementation of Distill, a
domain-specific compilation tool based on LLVM to accelerate cognitive models.
Cognitive models explain the process of cognitive function and offer a path to
human-like artificial intelligence. However, cognitive modeling is laborious,
requiring composition of many types of computational tasks, and suffers from
poor performance as it relies on high-level languages like Python. In order to
continue enjoying the flexibility of Python while achieving high performance,
Distill uses domain-specific knowledge to compile Python-based cognitive models
into LLVM IR, carefully stripping away features like dynamic typing and memory
management that add overheads to the actual model. As we show, this permits
significantly faster model execution. We also show that the code so generated
enables using classical compiler data flow analysis passes to reveal properties
about data flow in cognitive models that are useful to cognitive scientists.
Distill is publicly available, is being used by researchers in cognitive
science, and has led to patches that are currently being evaluated for
integration into mainline LLVM.
