Advanced Ultra Low-Power Deep Learning Applications with Neuromorphic Computing

The latest Intel neuromorphic processor, Loihi 2, provides a breakthrough in Artificial Intelligence (AI) for computing at the edge, where sensor information is collected. The computing architecture does this by leveraging computations at the transistor level in a fashion analogous to the human brain's biological neural networks (vs. a Von Neumann compute architecture). The Loihi 2's high performance, small form factor, and low-power consumption makes it a unique capability that is well suited for use in devices. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various airborne platforms' applications. The recently released Loihi 2 and the novel research completed on this effort were combined to accelerate development and demonstration of a new concept of operation for machine learning at the edge. This research included the development of spiking neural networks (SNN) on sensor data representative of information sources from a small research platform. Our concept uses the representative sensor data to predict the platform mode through machine learning. Importantly, our technical approach allowed us to rapidly scale from IBM's TrueNorth Corelet framework to the Lava framework, which Intel's Loihi 2 neuromorphic processor utilizes. The use of the Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on small airborne platform sensor data and wide extensibility to other domains that can use this neuromorphic compute hardware. In summary, this research included the use of new compute frameworks, novel processing algorithms, and a unique concept of operation. This technical approach resulted in the classification of the platform mode given the sensor information with accuracies up to 97.6%.