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 推理加速器启用循环级微架构仿真

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一个蓬勃发展的研究领域。虽然第一代刚性加速器提案使用为密集 DNN 量身定制的简单固定数据流，但最近的架构主张灵活有效地支持各种层类型、维度和稀疏性。随着这些加速器的复杂性增加，目前使用的分析模型证明无法捕捉执行时间的微妙之处，从而导致在许多情况下不准确。我们呈现斯通 ( 小号imulation TO的醇Ñ eural Ñ etwork ë ngines），对于国家的最先进的刚性和柔性DNN推理加速器可插入到任何高层DNN框架作为促进剂的装置，和执行周期级微架构模拟器密集和稀疏的真实、未修改的 DNN 模型的全模型评估。