Memory-augmented neural networks (MANNs) were introduced to handle long-term dependent data efficiently. MANNs have shown promising results in question answering (QA) tasks that require holding contexts for answering a given question. As demands for QA on edge devices have increased, the utilization of MANNs in resource-constrained environments has become important. To achieve fast and energy-efficient inference of MANNs, we can exploit application-specific hardware accelerators on field-programmable gate arrays (FPGAs). Although several accelerators for conventional deep neural networks have been designed, it is difficult to efficiently utilize the accelerators with MANNs due to different requirements. In addition, characteristics of QA tasks should be considered for further improving the efficiency of inference on the accelerators. To address the aforementioned issues, we propose an inference accelerator of MANNs on FPGA. To fully utilize the proposed accelerator, we introduce fast inference methods considering the features of QA tasks. To evaluate our proposed approach, we implemented the proposed architecture on an FPGA and measured the execution time and energy consumption for the bAbI data set. According to our thorough experiments, the proposed methods improved speed and energy efficiency of the inference of MANNs up to about 25.6 and 28.4 times, respectively, compared with those of CPU.

中文翻译：

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FPGA上的内存增强型神经网络，可实时高效节能地进行问答

引入了内存增强神经网络（MANN），可以有效地处理长期依赖的数据。MANN在回答问题（QA）任务中显示出令人鼓舞的结果，这些任务需要掌握用于回答特定问题的上下文。随着对边缘设备进行质量检查的需求增加，在资源受限的环境中使用MANN变得越来越重要。为了实现对MANN的快速且节能的推断，我们可以在现场可编程门阵列（FPGA）上利用专用硬件加速器。尽管已经设计了几种用于常规深度神经网络的加速器，但是由于需求不同，难以有效地将加速器与MANN一起使用。此外，应考虑QA任务的特征，以进一步提高对加速器的推理效率。为了解决上述问题，我们提出了一种基于FPGA的MANN推理加速器。为了充分利用提出的加速器，我们考虑了QA任务的特点，介绍了快速推理方法。为了评估我们提出的方法，我们在FPGA上实现了提出的架构，并测量了bAbI数据集的执行时间和能耗。根据我们的深入实验，与CPU相比，所提方法将MANN的推理速度和能效分别提高了约25.6和28.4倍。我们在FPGA上实现了建议的架构，并测量了bAbI数据集的执行时间和能耗。根据我们的深入实验，与CPU相比，所提方法将MANN的推理速度和能效分别提高了约25.6和28.4倍。我们在FPGA上实现了建议的体系结构，并测量了bAbI数据集的执行时间和能耗。根据我们的深入实验，与CPU相比，所提方法将MANN的推理速度和能效分别提高了约25.6和28.4倍。