The objective of this work is to develop highly Size, Weight, and Power (SWaP) efficient neuromorphic processors that can train deep learning algorithms. The training phase for deep learning is very compute and data intensive. Being able to train a network on the satellite eliminates the need to send large volumes of data to earth for training a new network. However, this requires an extremely energy efficient deep learning training processor. We will develop resistive crossbar neuromorphic processors, with the primary target being to train deep learning algorithms. We will look at multiple type of networks, including for cognitive communication applications, anomaly detection, and imaging. We will also look at processing networks for other data sets. The key outcomes of the work will be the processor design, processor performance metrics on various applications, prototype system, and software for the processor. Anticipated
Benefits: Potential NASA applications include various deep learning training and inference tasks on satellites. These include cognitive communications, processing sensor outputs, and scientific experiments. Additionally, the developed system could 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, cognitive decision making, and federated learning. Commercial applications include communications systems, automobiles, consumer electronics, and robots.
