Low SWaP real-time edge processing for cognitive sensing and autonomous control applications

Sanaz Adl; Adour V. Kabakian; Peter Petre; Austin Garrido

doi:10.1117/12.2576958

5 March 2021 Low SWaP real-time edge processing for cognitive sensing and autonomous control applications

Sanaz Adl, Adour V. Kabakian, Peter Petre, Austin Garrido

Proceedings Volume 11703, AI and Optical Data Sciences II; 1170306 (2021) https://doi.org/10.1117/12.2576958
Event: SPIE OPTO, 2021, Online Only

Abstract

Many real-time cognitive sensing signal processing and control applications require low SWAP edge processors with ultra-low latency adaptation and learning capabilities along with strict throughput, accuracy and power requirements. Achieving 3rd generation AI capabilities, i.e., real-time contextual adaptation, requires fast adaptive inference operations at low power beyond what is achievable with currently available neural networks and deep learning systems. While there has been tremendous progress in the form of edge accelerators, today’s processors lack capabilities for real-time processing, adaptation to novel situations, and low latency decision making. This paper addresses currently unsolved critical challenges in real-time cognitive sensing and autonomous system control applications, such as ultra-wide bandwidth and real-time signal denoising, anomaly detection, blind signal separation, and adaptive system equalization and control. We also present experimental results for low Cost – Size Weight and Power (C-SWAP) hardware implementation of an edge processor prototype implemented on a commercially available FPGA board.

Conference Presentation

Citation Download Citation

Sanaz Adl, Adour V. Kabakian, Peter Petre, and Austin Garrido "Low SWaP real-time edge processing for cognitive sensing and autonomous control applications", Proc. SPIE 11703, AI and Optical Data Sciences II, 1170306 (5 March 2021); https://doi.org/10.1117/12.2576958

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