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.

中文翻译：

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TDO-CIM：内存计算的透明检测和卸载

内存计算是一种很有前途的非冯诺依曼方法，旨在完全减少进出内存子系统的数据传输。尽管已经提出了很多架构，但对此类架构的编译器支持仍然滞后。在本文中，我们通过提出基于 LLVM 编译器基础架构的内存计算的端到端编译流程来弥补这一差距。从顺序代码开始，我们的方法会自动检测、优化和卸载适合内存加速的内核。我们在 PolyBench/C 基准套件上演示了我们的编译器工具流程，并通过将其与最先进的冯诺依曼架构进行比较来评估我们在 Gem5 中模拟的内存架构的优势。