Deep learning software demands reliability and performance.

However, many of the existing deep learning frameworks are software libraries
that act as an unsafe DSL in Python and a computation graph interpreter, some
with inefficient algorithmic differentiation by operator overloading.

We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain-specific optimizations and a code generator targeting GPU via LLVM.

Designed as a modern compiler framework inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity.

We argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.