DARPA Awards Funding to Build AI-Based Digital Phased Arrays to Reduce Processing Time and Cost

DARPA Awards Funding to Build AI-Based Digital Phased Arrays to Reduce Processing Time and Cost712370

Julia Computing has got funding from the US Department of Defense Advanced Research Projects Agency (DARPA) to bring Julia’s advanced artificial intelligence (AI) and machine learning (ML) capabilities to the field of fully digital phased array systems. Funding was awarded as part of DARPA’s Tensors for Reprogrammable Intelligent Array Demonstrations (TRIAD) program.

According to DARPA, in current digital phased arrays, every element is digitized, leading to an explosion of data requiring billions to trillions of complex operations per second. Currently, racks of high-powered processors are used in many stages of processing to handle the data processing challenge. TRIAD will create a streamlined processing approach to manage the beam forming and information processing directly within the array to significantly cut down on processing time and cost.

“In recent years, Phased Array Radio Frequency (RF) systems have found increasing popularity, from the SpaceX Starlink antenna to modern consumer communications standards like 5G and Institute of Electrical and Electronics Engineers (IEEE) 802.11 Wi-Fi,” says project Principal Investigator and Julia Computing CTO Keno Fischer. “With the increasing availability of low-cost radio integrated circuits (ICs) with excellent performance characteristics, the further applicability of phased array systems is now highly constrained by the availability of high-performance signal processing systems capable of handling the high data rates produced by all digital phased arrays. In this research program, we’re excited to bring Julia’s industry-leading graphics processing unit (GPU) compute capabilities to this rapidly growing domain.”

This project is a further step in Julia Computing’s extensive machine learning research program. “We are particularly excited by the possibility of integrating these capabilities into the larger Julia machine learning ecosystem,” says Julia Computing’s Dr. Elliot Saba, who is serving as co-PI on the project. “Because Julia provides compositionality by default, as well as language-level differentiable programming, we will be able to create a fully integrated system that performs both traditional signal processing, as well as novel ML-based inference simultaneously and in near-real time. This opens up a significant opportunity space to further enhance the performance of digital phased arrays, as well as extend this work to novel research areas such as bio-sensing radar.”

Julia Computing is partnering with RF expert Professor Miguel Bazdresch at the Rochester Institute of Technology to demonstrate these capabilities on purpose-built phased array testbeds, constructed from low-cost massive multiple input and multiple output (MIMO) Software Defined Radios and NVIDIA GPUs.

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Publisher: everything RF