  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.

Computer Vision and Pattern Recognition;Hardware Architecture