Title: Evaluation Mappings of Spatial Accelerator Based On Data Placement Show Chinese title

Title: 基于数据放置的空间加速器评估映射

Abstract: The scheduling strategies of workloads are critical to fully exploiting the performance of spatial accelerators, accurate performance models are required to evaluate the mapping of workloads.Recent works proposed various cost-model to describe the dataflow of the spatial accelerator. However, they are less expressive about customized memory hierarchies and thus lead to inaccurate performance models. In this paper, we propose, PolyAcc, a framework for evaluating the mappings of workload on spatial accelerator based on data placement. The Data placement relation describes the temporal-spatial relation of data at different memory levels, which can accurately capture the runtime behavior of hardware units. Based on data placement relations, polyAcc accurately analyzes the data volume for different reuse patterns and estimate metrics, including data reuse, latency, and energy. Overall, polyAcc closely matches the ideal execution time and PE utilization for GEMM and Conv workloads, respectively achieves 0.82%, 18.8% improvements for execution time and energy consumption estimates in validation against Eyeriss architecture compared to the state-of-the-art framework. Show Chinese abstract

Abstract: 工作负载的调度策略对于充分挖掘空间加速器的性能至关重要，需要准确的性能模型来评估工作负载的映射。最近的研究提出了各种成本模型来描述空间加速器的数据流。然而，它们在定制化内存层次结构方面的表达能力较弱，从而导致性能模型不准确。在本文中，我们提出了一种名为PolyAcc的框架，该框架基于数据放置来评估工作负载在空间加速器上的映射。数据放置关系描述了不同内存层级的数据的时空关系，可以准确捕捉硬件单元的运行时行为。基于数据放置关系，PolyAcc能够准确分析不同重用模式的数据量，并估算包括数据重用、延迟和能耗在内的指标。总体而言，PolyAcc分别与GEMM和Conv工作负载的理想执行时间和PE利用率高度匹配，在与最先进的框架相比的验证中，针对Eyeriss架构，执行时间和能耗估计分别实现了0.82%和18.8%的改进。