CINM (Cinnamon)：面向异构计算存内与近存计算范式的编译基础设施
CINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigms
AI总结：
针对异构CIM/CNM架构可编程性差的问题，提出首个端到端编译流程CINM，利用层次化抽象支持设备无关与设备感知优化，在UPMEM和忆阻器CIM上生成媲美或超越最先进实现的高性能代码。
摘要：
数据密集型应用的兴起暴露了传统以处理器为中心的冯·诺依曼架构的局限性，这类架构难以满足片外存储带宽需求。因此，近期计算机架构领域的创新倡导计算存内（CIM）和计算近存（CNM）这两种非冯·诺依曼范式，它们在性能和能耗方面实现了数量级的提升。尽管过去几年取得了重大的技术突破，但这些系统的可编程性仍然是一个严峻的挑战。它们的编程模型过于底层，且特定于具体的系统实现。由于这类未来架构预计将高度异构，开发新颖的编译器抽象和框架变得十分必要。为此，我们提出了CINM（Cinnamon），这是首个端到端的编译流程，它利用层次化抽象来泛化不同的CIM和CNM设备，并支持设备无关和设备感知的优化。Cinnamon逐步降低输入程序，并在降低流水线的每一层执行优化。为了展示其有效性，我们在知名的UPMEM CNM系统和基于忆阻器的CIM加速器上的一组基准测试中评估了CINM。我们证明，支持多种硬件目标的Cinnamon能够生成与最先进实现相当或更优的高性能代码。
英文摘要：
The rise of data-intensive applications exposed the limitations of conventional processor-centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth demand. Therefore, recent innovations in computer architecture advocate compute-in-memory (CIM) and compute-near-memory (CNM), non-von- Neumann paradigms achieving orders-of-magnitude improvements in performance and energy consumption. Despite significant technological breakthroughs in the last few years, the programmability of these systems is still a serious challenge. Their programming models are too low-level and specific to particular system implementations. Since such future architectures are predicted to be highly heterogenous, developing novel compiler abstractions and frameworks become necessary. To this end, we present CINM (Cinnamon), a first end-to-end compilation flow that leverages the hierarchal abstractions to generalize over different CIM and CNM devices and enable device-agnostic and device-aware optimizations. Cinnamon progressively lowers input programs and performs optimizations at each level in the lowering pipeline. To show its efficacy, we evaluate CINM on a set of benchmarks for the well-known UPMEM CNM system and the memristors-based CIM accelerators. We show that Cinnamon, supporting multiple hardware targets, generates high-performance code comparable to or better than state-of-the-art implementations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
