Spike-Driven YOLO: Ultra Low-Power Object Detection 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 high performance, small form factor, and low-power consumption make it well-suited for a wide-range of real-time, deep learning applications such as target classification, object detection, and more. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various ground and airborne platforms' applications. The recently released Loihi 2 ecosystem and the thorough research study completed on this effort were combined to accelerate development, optimization, and demonstration of a new concept of operation for machine learning at the edge. This 2024 research included training and testing Spike-driven YOLO models on data from various sensors. Our concept uses representative sensor data to detect and classify targets of interest through a combination of image processing techniques and machine learning. Importantly, our technical approach allowed us to rapidly train and evaluate the performance of several models for benchmarking against current state-of-the-art algorithms - w/mean average precision > 93% in some cases. The use of Intel's latest Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on several sensor platforms with wide extensibility to other domains that can use this neuromorphic-computing hardware. In summary, this research included the use of new computing frameworks, processing algorithms, and a unique concept of operation.