High-Performance Computing (HPC) and Machine Learning Demonstrated in Flight Using Agile Condor®

For the first time ever, advanced machine learning (ML) compute architectures, techniques, and methods were demonstrated in flight (in June-August 2017 and May 2018) on the recently invented high-performance computing (HPC) architecture called Agile Condor (U.S. Patent Pending #5497944). The Air Force Research Laboratory (AFRL) Information Directorate Advanced Computing and Communications Division continues to develop and demonstrate new computing architectures, designed to provide HPC ground and airborne (pod-based) solutions to meet operational and tactical, real-time processing for intelligence, surveillance, and reconnaissance (ISR) mission needs. Agile Condor is a scalable system based on open industry standards that continues to demonstrate the ability to increase, far beyond the current state-of-the-art, computational capability within the restrictive size, weight and power (SWaP) constraints of unmanned aircraft systems' external “pod” payloads. This system is enabling the exploration and development of innovative system solutions to meet future Air Force real-time HPC needs; e.g., multi-mission and multifunction ISR processing and exploitation. The Agile Condor system innovations include: (1) a cost-effective and flexible compute architecture, (2) support for multiple missions, (3) facilitating realistic, repeatable experimentation, and (4) enabling related experimentation and applications for operational exploitation of a wide range of information products. On the recent collection, demonstration and data collection efforts, information was simultaneously processed in a parallelized approach using two distinct ML approaches. This approach enabled real-time trade-space analyses and the ability to immediately contrast and compare the approaches. The data processing also included the exploitation of data from multiple sensors, such as optical, full-motion video (FMV), and radar. Thereby, Agile Condor's heterogenous computing architecture continues to accelerate the development of computing technologies and ML algorithms necessary to exploit large quantities of data on a platform upstream, closer to the sensors. The prototype ML techniques that can be utilized include, but are not limited to, specialized deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN) that support sequential/temporal data products and applications for exploitation, pattern recognition and autonomous operation.