  Computation in-memory is a promising non-von Neumann approach aiming at
completely diminishing the data transfer to and from the memory subsystem.
Although a lot of architectures have been proposed, compiler support for such
architectures is still lagging behind. In this paper, we close this gap by
proposing an end-to-end compilation flow for in-memory computing based on the
LLVM compiler infrastructure. Starting from sequential code, our approach
automatically detects, optimizes, and offloads kernels suitable for in-memory
acceleration. We demonstrate our compiler tool-flow on the PolyBench/C
benchmark suite and evaluate the benefits of our proposed in-memory
architecture simulated in Gem5 by comparing it with a state-of-the-art von
Neumann architecture.
