The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. While first-generation rigid accelerator proposals used simple fixed dataflows tailored for dense DNNs, more recent architectures have argued for flexibility to efficiently support a wide variety of layer types, dimensions, and sparsity. As the complexity of these accelerators grows, the analytical models currently being used prove unable to capture execution-time subtleties, thus resulting inexact in many cases. We present STONNE ( Simulation TOol of Neural Network Engines ), a cycle-level microarchitectural simulator for state-of-the-art rigid and flexible DNN inference accelerators that can plug into any high-level DNN framework as an accelerator device, and perform full-model evaluation of both dense and sparse real, unmodified DNN models.

中文翻译：

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STONNE：为 DNN 推理加速器启用周期级微架构仿真


用于加速深度神经网络（DNN）推理过程的专用架构的设计是当今蓬勃发展的研究领域。虽然第一代刚性加速器建议使用为密集 DNN 定制的简单固定数据流，但最近的架构主张灵活地有效支持各种层类型、维度和稀疏性。随着这些加速器的复杂性不断增加，目前使用的分析模型无法捕捉执行时间的微妙之处，从而在许多情况下导致不精确。我们推出了 STONNE（神经网络引擎模拟工具），这是一种用于最先进的刚性和柔性 DNN 推理加速器的循环级微架构模拟器，可以作为加速器设备插入任何高级 DNN 框架，并执行完整的任务。 -密集和稀疏真实、未经修改的 DNN 模型的模型评估。