Deep neural networks (DNNs) come with many forms, such as convolutional neural networks, multilayer perceptron, and recurrent neural networks, to meet diverse needs of machine learning applications. However, existing DNN accelerator designs, when used to execute multiple neural networks, suffer from underutilization of processing elements, heavy feature map traffic, and large area overhead. In this article, we propose a novel approach, Polymorphic Accelerators , to address the flexibility issue fundamentally. We introduce the abstraction of logical accelerators to decouple the fixed mapping with physical resources. Three procedures are proposed that work collaboratively to reconfigure the accelerator for the current network that is being executed and to enable cross-layer data reuse among logical accelerators. Evaluation results show that the proposed approach achieves significant improvement in data reuse, inference latency and performance, e.g., 1.52x and 1.63x increase in throughput compared with state-of-the-art flexible dataflow approach and resource partitioning approach, respectively. This demonstrates the effectiveness and promise of polymorphic accelerator architecture.

中文翻译：

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深度神经网络的多态加速器

深度神经网络 (DNN) 有多种形式，例如卷积神经网络、多层感知器和循环神经网络，以满足机器学习应用的多样化需求。然而，现有的 DNN 加速器设计在用于执行多个神经网络时，会遇到处理元素利用率低、特征图流量大和面积开销大的问题。在本文中，我们提出了一种新颖的方法，多态加速器，从根本上解决灵活性问题。我们引入了逻辑加速器的抽象来将固定映射与物理资源解耦。提出了三个程序，它们协同工作，为正在执行的当前网络重新配置加速器，并实现逻辑加速器之间的跨层数据重用。评估结果表明，所提出的方法在数据重用、推理延迟和性能方面取得了显着改善，例如，与最先进的灵活数据流方法和资源划分方法相比，吞吐量分别提高了 1.52 倍和 1.63 倍。这证明了多态加速器架构的有效性和前景。