Low-power, ultra-fast deep learning neuromorphic chip for unmanned aircraft systems

Artificial Intelligence (AI) is driving the fourth industrial revolution as well as permeating every aspect of our day-to-day life. From big data analysis to language analysis and real time translation, from speech recognition to image recognition. The latter is a powerful and quite general application with a scope that spans from medical imaging to autonomous driving and to military applications. Mentium Technologies Inc., spun from a UC Santa Barbara research lab in the Electrical and Computer Engineering department is committed to embrace the AI revolution strong of the experience of its team in the neuromorphic hardware for AI. Indeed, we will develop a neuromorphic chip able to do higher than real-time image recognition and/or object classification on board the UAS. The chip will use 1/100th of the energy while reaching 100x in speed compared to state of the art. The team already had demonstrated 1000x and 1/1000th energy consumption in a smaller scale experimental demo. From this experience UCSB has a patented technology licensed by Mentium Technologies Inc. thanks to this technology and its develpment within this project, the Neuromorphic Chip will empower the UAS with Cognitive functions enabling autonomous guidance, decision making and complex image processing, while keeping the power consumption low.

Compared to conventional and classical signal processing algorithms, the process done in Deep Neural Networks (DNNs) resembles more to what is happening in human brain and because of that, these networks can provide more useful insight and perception form the surrounding environment. Moreover, by following a teacher like a crew member, they can learn by themselves to how to react autonomously in different and complex situations. All these properties make them a valuable technology to help NASA automates earth and space missions. Here are some of the applications of DNN in NASA-related missions: - Aircraft control - Damage-adaptive decision making - Detect extreme weather in climate datasets - Classification of aerial images - Fire detection and control - Autonomous driving of vehicles for space missions - Automatic feature extraction from large datasets of probes images

Deep neural networks are envisioned to revolutionize the field of machine learning and their applications because they provide a simple platform to achieve performances beyond what could have been achieved with conventional digital Von Neumann architectures. In fact, despite of being a very young technology, it is already in use in so many commercial applications including but not limited to: - Google is using DNNs for speech recognition in Alexa, for image recognition to diagnose diseases from medical images, for object detection in its self-driving cars, for translating text from one language to another one, etc. - Baidu is using DNN to automatically convert speech to text in mobile phones - The hand-written text on all checks and envelops are automatically read with DNNs - In the huge market of advertising, DNN are now helping to identify the potential costumers for a particular product - All face recognitions and people identifications happening in Facebook pages are possible only because of DNNs - Automatic game playing - Automatic image caption generation

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