We review HyDRATE, a low-SWaP reconfigurable neural network architecture developed under the DARPA AIE HyDDENN (Hyper-Dimensional Data Enabled Neural Network) program. We describe the training and simulated performance of a feature extractor free of multiply-accumulates (MAC) feeding a hyperdimensional (HD) logic-based classifier and show how performance increases with the number of hyperdimensions. Reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation. We show performance for a video activity classification task and demonstrate retraining on this same dataset. Finally, we discuss a realized FPGA architecture that achieves 10x smaller memory footprint, 10x simpler operations and 100x lower latency/power compared to traditional deep neural networks.
We present a camera system for instantaneous, non-destructive capture of spectral signatures for forensic analysis. Our system detects highly probative samples in the forensic scene mixed by the multiple target objects by combining a coded aperture snapshot spectral imager with a multi-spectral detection algorithm. An Adaptive Cosine Estimator (ACE) is used to quantitatively detect and classify the probative samples from the decoded spectral datacube. In this paper, we demonstrate selected results using our system for luminescence characteristics and spectral classification of a number of samples.