Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

• URL: http://arxiv.org/abs/2206.05128v1
• Date: Fri, 10 Jun 2022 14:08:41 GMT
• Title: Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
• Authors: Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels, Gooitzen van der Wal, Aswin Raghavan, Yuzheng Zhang, Jun Hu, Michael Lomnitz, Michael Isnardi, David Zhang, Michael Piacentino
• Abstract summary: HyDRATE can perform real-time reconfiguration at the edge using 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. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without descent gradient backpropagation.
• Score: 12.599871451119538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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