CLSA-CIM：一种面向存内计算架构的跨层调度方法
CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in-Memory Architectures
AI总结：
本文提出面向分块存内计算架构的跨层调度算法CLSA-CIM，通过提高CIM核心利用率，相比现有最优方法实现最高29.2倍加速。
摘要：
对高效机器学习（ML）加速器的需求正在迅速增长，推动了诸如基于电阻式随机存取存储器（RRAM）的分块存内计算（CIM）架构等新型计算概念的发展。CIM允许在存储单元内部进行计算，从而实现更快的数据处理和更低的功耗。高效的编译器算法对于发挥分块CIM架构的潜力至关重要。传统ML编译器主要针对CPU、GPU和其他冯·诺依曼架构进行代码生成，而针对CIM架构则需要进行相应适配。跨层调度是一种很有前景的方法，因为它能够提高CIM核心的利用率，从而加速计算。尽管类似概念在先前工作中已被隐式使用，但对于分块CIM架构的跨层调度仍缺乏清晰且可量化的算法定义。为填补这一空白，我们提出了CLSA-CIM，一种面向分块CIM架构的跨层调度算法。我们将CLSA-CIM与现有的权重映射策略集成，并与最先进的（SOTA）调度算法进行性能比较。CLSA-CIM将利用率最高提升17.9倍，与SOTA相比，整体加速比最高提升29.2倍。
英文摘要：
The demand for efficient machine learning (ML) accelerators is growing rapidly, driving the development of novel computing concepts such as resistive random access memory (RRAM)-based tiled computing-in-memory (CIM) architectures. CIM allows to compute within the memory unit, resulting in faster data processing and reduced power consumption. Efficient compiler algorithms are essential to exploit the potential of tiled CIM architectures. While conventional ML compilers focus on code generation for CPUs, GPUs, and other von Neumann architectures, adaptations are needed to cover CIM architectures. Cross-layer scheduling is a promising approach, as it enhances the utilization of CIM cores, thereby accelerating computations. Although similar concepts are implicitly used in previous work, there is a lack of clear and quantifiable algorithmic definitions for cross-layer scheduling for tiled CIM architectures. To close this gap, we present CLSA-CIM, a cross-layer scheduling algorithm for tiled CIM architectures. We integrate CLSA-CIM with existing weight-mapping strategies and compare performance against state-of-the-art (SOTA) scheduling algorithms. CLSA-CIM improves the utilization by up to 17.9 x , resulting in an overall speedup increase of up to 29.2 x compared to SOTA.
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