Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation approach for fast performance models to accurately estimate the latency of a DNN mapped onto systematically modeled and concisely described accelerator architectures. Using our accelerator architecture description method, we modeled representative DNN accelerators such as Gemmini, UltraTrail, Plasticine-derived, and a parameterizable systolic array. Together with DNN mappings for those modeled architectures, we perform a combined DNN/hardware dependency graph analysis, which enables us, in the best case, to evaluate only 154 loop kernel iterations to estimate the performance for 4.19 billion instructions achieving a significant speedup. We outperform regression and analytical models in terms of mean absolute percentage error (MAPE) compared to simulation results, while being several magnitudes faster than an RTL simulation.

本研究针对在资源受限的边缘设备上实现深度神经网络（DNN）面临的挑战，提出了一种自动生成快速性能模型的方法，以准确估算映射到加速器架构的DNN延迟。我们的关键发现是，该方法结合DNN硬件依赖图分析，显著加速了性能评估过程，相较于仿真结果，有效降低了平均绝对百分比误差（MAPE）。

深度神经网络加速器快速准确性能模型的自动生成