Deepwave releases AI radio developed for RF deep learning applications

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July 27, 2018

Image by Deepwave Digital

PHILADELPHIA. Representatives of Deepwave Digital, Inc. released an Artificial Intelligence Radio Transceiver (AIR-T), a Software Defined Radio (SDR) designed and developed for radio frequency (RF) deep learning applications.

Deepwave Digital’s goal is to enable the next generation of RF systems to use deep learning by combining high-performance computing (HPC) with SDR in one embedded platform. See the article : Software Defined Radio (SDR) for Indian Army under Aatmanirbhar Bharat Abhiyaan. In response to the limited number of applications that have been demonstrated, deep learning algorithms within wireless technologies have not yet reached widespread adoption due to the lack of readily available hardware platforms, limited software tools, and the abstract nature of AI algorithms compared to traditional digital processing. of signals (DSP).

AIR-T is a development and deployment SDR that couples a 2×2 multiple-input-multiple-output (MIMO) transceiver with a triad of signal processors: a Xilinx field programmable gate array (FPGA), an embedded central processing unit (CPU), and an embedded NVIDIA GPU.

John Ferguson, CEO of Deepwave Digital, says: “It can be used as an AI signal identification receiver, a small cell wireless node or an ad hoc wireless signal data interpreter. Alternatively, the FPGA can be used for traditional demodulation and the GPU for deep learning algorithms. For example, the FPGA will demodulate a video signal, sending video frames to the embedded GPU, where an existing computer vision algorithm is used. Right now, the number of ways to use AIR-T, using AI in an RF system is an untapped technology market.”

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