FPGA-based Deep Learning Accelerator for RF Applications

H. den Boer, R.W.D. Muller, J.S.S.M. Wong, V. Voogt

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)
Subtitle of host publicationProceedings
Place of PublicationDanvers
Number of pages6
ISBN (Electronic)978-1-6654-3956-5
ISBN (Print)978-1-6654-3972-5
Publication statusPublished - 2021
Event2021 IEEE Military Communications Conference (MILCOM) - San Diego, United States
Duration: 29 Nov 20212 Dec 2021


Conference2021 IEEE Military Communications Conference (MILCOM)
Abbreviated titleMILCOM 2021
Country/TerritoryUnited States
CitySan Diego


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

Cite this