Invited Paper: In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar

Yang Sui, Minning Zhu, Lingyi Huang, Chung Tse Michael Wu, Bo Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-scale RFNN in practice requires an extensive number of RF interferometers and consumes a substantial amount of energy. To address this challenge, we propose to utilize low-rank decomposition to transform a large-scale RFNN into a compact RFNN while almost preserving its accuracy. Specifically, we develop a Tensor-Train RFNN (TT-RFNN) where each layer comprises a sequence of low-rank third-order tensors, leading to a notable reduction in parameter count, thereby optimizing RF interferometer utilization in comparison to the original large-scale RFNN. Additionally, considering the inherent physical errors when mapping TT-RFNN to RF device parameters in real-world deployment, from a general perspective, we construct the Robust TT-RFNN (RTT-RFNN) by incorporating a robustness solver on TT-RFNN to enhance its robustness. To adapt the RTT-RFNN to varying requirements of reshaping operations, we further provide a reconfigurable reshaping solution employing RF switch matrices. Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the effectiveness of our proposed method.

Original languageEnglish (US)
Title of host publication2023 42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350315592
DOIs
StatePublished - 2023
Externally publishedYes
Event42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023 - San Francisco, United States
Duration: Oct 28 2023Nov 2 2023

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023
Country/TerritoryUnited States
CitySan Francisco
Period10/28/2311/2/23

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Keywords

  • Radio frequency neural network
  • efficiency
  • model compression
  • robustness
  • tensor-train decomposition

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