Computing-in-Memory (CIM) architectures using Non-volatile Memories (NVMs) have emerged as a promising way to address the “memory wall” problem in traditional Von Neumann architectures. CIM accelerators can perform arithmetic or Boolean logic operations in NVMs by fully exploiting their high parallelism for bit-wise operations. These accelerators are often used in cooperation with general-purpose processors to speed up a wide variety of artificial neural network applications. In such a heterogeneous computing architecture, the legacy software should be redesigned and re-engineered to utilize new CIM accelerators. In this article, we propose a compilation tool to automatically migrate legacy programs to such heterogeneous architectures based on the low-level virtual machine (LLVM) compiler infrastructure. To accelerate some computations such as vector-matrix multiplication in CIM accelerators, we identify several typical computing patterns from LLVM intermediate representations , which are oblivious to high-level programming paradigms. Our compilation tool can modify accelerable LLVM IRs to offload them to CIM accelerators automatically, without re-engineering legacy software. Experimental results show that our compilation tool can translate many legacy programs to CIM-supported binary executables effectively, and improve application performance and energy efficiency by up to 51× and 309×, respectively, compared with general-purpose x86 processors.