  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.
