论文标题
使用硬件加速器在边缘的实时超维重新配置
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
论文作者
论文摘要
在本文中，我们使用低S型嵌入式硬件在战术边缘（水合物）上介绍了超维的可重构分析，该硬件可以在边缘进行实时重新配置，从而利用非MAC（无浮点多裂库污染操作），深度神经网（DNN）结合了超二级（DNN），结合了超二级（Hdimensimentional（hd）计算机的计算机。我们描述了算法，经过训练的量化模型生成以及特征提取器的模拟性能，不含多重蓄能的功能，从而喂养基于高维逻辑的分类器。然后，我们展示了性能如何随着超数的数量而增加。我们将与传统DNN相比，描述已实现的低SWAP FPGA硬件和嵌入式软件系统，并详细介绍实现的硬件加速器。我们讨论了测量的系统延迟和功率，由于使用可学习的量化和高清计算而引起的噪声稳健性，用于视频活动分类任务的实际和模拟系统性能以及在同一数据集上重新配置的演示。我们表明，通过仅使用梯度下降反向传播（无梯度）的馈电HD分类器（无梯度），可以通过使用几乎没有镜头的新课程来实现现场的可重构性。进行的初始工作使用了LRCN DNN，目前已扩展到使用具有提高性能的两流DNN。
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance.
