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On March 19, the Defense Advanced Research Projects Agency (DARPA) released a special notice about an upcoming Artificial Intelligence Exploration Opportunity: Tensors for Reprogrammable Intelligent Array Demonstrations (TRIAD).
The purpose of this Special Notice (SN) is to provide public notification of additional research areas of interest to the Defense Advanced Research Projects Agency (DARPA), specifically the Artificial Intelligence Exploration (AIE) program. The mission of the Defense Advanced Research Projects Agency (DARPA) is to make strategic, early investments in science and technology that will have long-term positive impact on our Nation’s security. In support of this mission, DARPA has pioneered groundbreaking research and development (R&D) in Artificial Intelligence (AI) for more than five decades. Today, DARPA continues to lead innovation in AI research through a large, diverse portfolio of fundamental and applied R&D AI programs aimed at shaping a future for AI technology where machines may serve as trusted and collaborative partners in solving problems of importance to national security.
The AI Exploration (AIE) program is one key element of DARPA’s broader AI investment strategy that will help ensure the U.S. maintains a technological advantage in this critical area. Past DARPA AI investments facilitated the advancement of “first wave” (rule based) and “second wave” (statistical learning based) AI technologies. DARPA-funded R&D enabled some of the first successes in AI, such as expert systems and search, and more recently has advanced machine learning algorithms and hardware. DARPA is now interested in researching and developing “third wave” AI theory and applications that address the limitations of first and second wave technologies.
At this time, the DARPA Strategic Technology Office (STO) is interested in the following research area to be announced as a potential AIE topic under the Artificial Intelligence Exploration program:
Tensors for Reprogrammable Intelligent Array Demonstrations (TRIAD): Electromagnetic (EM) phased arrays contain many antenna elements that transmit and receive EM signals. These signals are combined in such a way as to manipulate the spatial directions of the signals into beams, such as to transmit a signal in a specific direction or receive only those signals of interest from a desired direction, while ignoring signals emanating from other directions. The fundamental math operation behind phased arrays is a complex multiplication of rows of coefficients, one per beam, by every array element. The beam forming operation is followed by information extraction and processing. 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.
TRIAD takes a new look at phased array processing by formulating the coefficient multiplication into a linear algebra tensor operation, which is also the fundamental operation behind statistical machine learning (ML) (e.g., deep learning, including deep neural networks). TRIAD benefits from three advances in tensor processing: tensor algorithm development and execution on specialized processors, such as graphics processing units (GPUs), tensor abstractions into common programming frameworks, such as PyTorch to speed up application development, and ongoing reduction in the costs of constructing all-digital phased arrays. Combining EM array processing, new computational architectures, and tensor/ML-based programming environments, TRIAD will revolutionize array processing.
TRIAD will require performers to devise architectures that support array applications and computations through tensor operations. These architectures are to be drawn from commercially available GPUs, FPGAs, multi-core CPUs, etc. that will comprise the array computational backplane. Performers must also devise tensor libraries specifically oriented towards array operations, from fundamental beam forming support to more advanced operations involving machine learning and quasi-optical analyses. TRIAD will likewise require prototype array development demonstrating TRIAD capabilities.
Full information is available here.
Source: SAM
