Technical Abstract (Limit 2000 characters, approximately 200 words):To meet the NASA need for power efficient algorithms that improve onboard autonomy, Exploration Institute proposes to develop NERVES, an approach for power efficient, verifiable generic calculation and signals processing onboard resource constrained systems using the latest neuromorphic hardware.
Through our substantial experience in applying and developing neuromorphic algorithms for spacecraft systems, we have determined that the performance of our algorithms can be substantially improved if the lowest level substrate, the building blocks, were designed to use the most power efficient traits of neuromorphic hardware. More efficient performance of key mathematical operations in neuromorphic hardware would provide high value to spacecraft developers as they can translate their existing work directly into a vastly more power efficient and faster processing system. NERVES is driven by a practical need and the pipeline to commercialization is already established through Exploration Institute’s track record and current work.
NERVES directly maps conventional algorithms to any neuromorphic processor, combining the benefits of more capable, well known algorithms with the power savings of a neuromorphic architecture. A neuromorphic chip like Intel’s Loihi has a computational power density of more than 1000x that of a CPU or GPU for some tasks. Based on our initial analysis, Exploration Institute predicts that NERVES will enable these kinds of power savings (or conversely, computational capacity increase for the same power) which will greatly improve NASA's capacity for onboard autonomy.
As an added bonus, NERVES provides a more verifiabile approach to neuromorphic computing in space, by allowing the use of verified computing approaches (non-neuromorphic, conventional) on neuromorphic hardware with all the power savings that can entail. This enables more likely adoption and infusion into NASA programs.
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