SRI Authors: Michael A. Isnardi, David Zhang, Gooitzen van der Wal, Michael Piacentino
Michael Isnardi, Saurabh Farkya, Indu Kandaswamy, Aswin Raghavan, David Zhang, Gooitzen van der Wal, Joe Zhang, Zachary Daniels, Michael Piacentino, “Hyper-Dimensional Analytics of Video Action at the Tactical Edge”. Presented at the GOMACTech 2021 virtual conference, March 29 – April 1, 2021.
Abstract
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.
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