FPGA-based Deep Learning Accelerator for RF Applications

FPGA-based Deep Learning Accelerator for RF Applications

de Boer, H.
Muller, R.W.D.
Wong, S.
Voogt, V.

2021

A key obstacle within the design of cognitive radios has always been the spectrum sensing component that implements the function automatic modulation classification (AMC). With the transition to software-defined radios (SDRs) followed by the introduction of field-programmable gate arrays (FPGAs) and deep learning (DL), it becomes possible to surmount this obstacle. However, the design of DL models is still detached from synthesized FPGA designs in current implementation frameworks. Consequently, the design process is a tedious and lengthy one. In this paper, a novel implementation framework is presented for implementing deep learning inference models within signal processing chains on FPGAs. The framework focuses on optimization for radio-frequency (RF) transceiver applications, aiming for high-throughput, low latency and a small FPGA resource footprint enabling the scaling to larger DL models. Demonstration of the implementation framework for automatic modulation classification (AMC) results in an operational throughput of 585k classifications per second.

Artificial Intelligence
Deep Learning
FPGA
Inference
RF
Cognitive Radio
Software Defined Radio
Automatic Modulation Classification

http://resolver.tudelft.nl/uuid:3d36f0fc-a440-4eec-9995-7a798055e7fd

967438

MILCOM 2021 IEEE Mililtary Communications Conference

conference paper