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.

本文介绍了利用低功耗嵌入式硬件的超高维可重构分析系统 (HyDRATE)，该系统利用无 MAC (无浮点数乘积累加操作) 深度神经网络 (DNN) 和超维 (超高维) 计算加速器在边缘执行实时重新配置。通过展示该算法、训练好的量化模型生成和特征提取来实现超高纬度基于逻辑的分类器，并展示了性能随超维数增加而提高的情况。本文介绍了实现的低功耗 FPGA 硬件和嵌入式软件系统，和实现的硬件加速器，以及系统延迟和功耗噪声稳健性等方面的讨论。通过视频活动分类任务的实际与模拟系统性能比较，展示了在相同数据集上的重新配置。使用边缘的少量学习新课程，使用无梯度下降反向传播仅通过重新训练前馈超高维分类器实现该系统的重构性。