Fully-Homomorphic Encryption (FHE) offers powerful capabilities
by enabling secure offloading of both storage and computation,
and recent innovations in schemes and implementations have made
it all the more attractive. At the same time, FHE is notoriously
hard to use with a very constrained programming model, a very
unusual performance profile, and many cryptographic constraints.
Existing compilers for FHE either target simpler but less efficient
FHE schemes or only support specific domains where they can rely
on expert-provided high-level runtimes to hide complications.

This paper presents a new FHE language called Encrypted Vector
Arithmetic (EVA), which includes an optimizing compiler that
generates correct and secure FHE programs, while hiding all
the complexities of the target FHE scheme. Bolstered by our
optimizing compiler, programmers can develop efficient
general-purpose FHE applications directly in EVA. For example,
we have developed image processing applications using EVA,
with a very few lines of code.

EVA is designed to also work as an intermediate representation
that can be a target for compiling higher-level domain-specific
languages. To demonstrate this, we have re-targeted CHET, an
existing domain-specific compiler for neural network inference,
onto EVA. Due to the novel optimizations in EVA, its programs
are on average $5.3\times$ faster than those generated by CHET.
We believe that EVA would enable a wider adoption of FHE by
making it easier to develop FHE applications and domain-specific
FHE compilers.