Resistive Deep Learning Neuromorphic Processors

Artificial intelligence (AI) algorithms have many applications in satellites and are generally quite compute intensive. The objective of this work is to develop highly Size, Weight, and Power (SWaP) efficient neuromorphic processors that can run AI algorithms. We will develop resistive crossbar neuromorphic processors, with the primary target being deep learning algorithms. We plan to process various types of signals very efficiently – these include sensor and cognitive communication applications. The key outcomes of the work will be the processor design, processor performance metrics on various applications, and software for the processor.

Potential NASA applications include various deep learning tasks on satellites. These include processing sensor outputs and classifying communication modulations. Additionally, the developed system be used for UAVs.

The non-NASA market would be primarily for edge processing, where power is highly limited. The market includes both the DoD and the commercial market. DoD applications include cognitive communications, sensor processing, and cognitive decision making. Commercial applications include communications systems, automobiles, consumer electronics, and robots.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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