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.
翻译:在本文中,我们展示了在战术边缘(HyDraate)使用低SWaP嵌入硬件的超二元再配置分析器,这些硬件可以在边缘对非MAC(没有浮点多相叠合操作)的杠杆作用下进行实时重组,深神经网(DNN)与超维(HD)计算加速器结合使用。我们描述了算法、经过培训的四分制模型生成和模拟的功能提取器的功能提取器,该特性提取器不包含以超维逻辑为基础的分类器。然后,我们展示了超二分化器数量的性能如何提高。我们描述了与传统的DNNNCS相比,已经实现的低SWAP FPGA硬件和嵌入软件系统,并详细介绍了已安装的硬件加速器。我们讨论了测量的系统弹性和能量、由于使用可学习的四分解和HD计算而出现的噪音坚固度、用于视频活动分类任务的实际和模拟的系统性能,以及同一数据集的重组演示。我们显示,目前对外地的可重新配置性调整性能,只有采用不向后再升级的升级的升级,才进行后再升级,而进行后再升级。