Cambridge, MA – Julia Computing has been awarded funding by 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 WiFi,” 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.
Julia Computing is always looking to partner with innovative companies to bring its technology into real-world use. We encourage companies working on next-generation wireless systems to contact us at email@example.com to discuss how this technology might be able to help.
Julia Computing is also looking to hire a Wireless Systems Research Engineer and fill several other technical positions. For more information and to apply, please visit JuliaComputing.com/Jobs.
About Julia Computing and Julia
Julia Computing's mission is to develop products that bring Julia's superpowers to its customers. Julia Computing's flagship product is JuliaHub, a secure, software-as-a-service platform for developing Julia programs, deploying them, and scaling to thousands of nodes. It provides the power of a supercomputer at the fingertips of every data scientist and engineer. In addition to data science workflows, JuliaHub also provides access to cutting-edge products such as Pumas for pharmaceutical modeling and simulation, JuliaSim for multi-physics modeling and simulation, and JuliaSPICE for electronic circuit simulation, combining traditional simulation with modern SciML approaches.
Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded by users at more than 10,000 companies and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software and the 2019 Sidney Fernbach Award. Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.