TDO-CIM：内存计算的透明检测和卸载
TDO-CIM: Transparent Detection and Offloading for Computation In-memory
摘要：
内存计算是一种很有前途的非冯诺依曼方法，旨在完全减少数据传输到内存子系统和从内存子系统。尽管已经提出了很多架构，但编译器对这些架构的支持仍然很落后。在本文中，我们提出了一个基于LLVM编译器基础设施的存内计算的端到端编译流程，从而缩小了这一差距。从顺序代码开始，我们的方法自动检测，优化和卸载适合内存加速的内核。我们展示了我们的编译器工具流的PolyBench/C基准套件，并评估我们提出的内存中的架构模拟Gem 5的好处，通过比较它与一个国家的最先进的冯诺依曼架构。
英文摘要：
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.
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