Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Maolin Wang
  • Kelvin C. M. Lee
  • Bob M. F. Chung
  • Sharatchandra Varma Bogaraju
  • Ho-Cheung Ng
  • Justin S. J. Wong
  • Kevin K. Tsia
  • Hayden Kwok-Hay So

Detail(s)

Original languageEnglish
Pages (from-to)2853-2866
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume33
Issue number7
Online published12 Jan 2021
Publication statusPublished - Jul 2022
Externally publishedYes

Link(s)

Abstract

Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and have achieved very high inference throughput, they are not suitable for latency-sensitive applications where real-time feedback is needed. Here, we demonstrate that high-performance reconfigurable computing platforms based on field-programmable gate array (FPGA) processing can successfully bridge the gap between low-level hardware processing and high-level intelligent image analytics algorithm deployment within a unified system. The proposed design performs inference operations on a stream of individual images as they are produced and has a deeply pipelined hardware design that allows all layers of a quantized convolutional neural network (QCNN) to compute concurrently with partial image inputs. Using the case of label-free classification of human peripheral blood mononuclear cell (PBMC) subtypes as a proof-of-concept illustration, our system achieves an ultralow classification latency of 34.2 μs with over 95% end-to-end accuracy by using a QCNN, while the cells are imaged at throughput exceeding 29 200 cells/s. Our QCNN design is modular and is readily adaptable to other QCNNs with different latency and resource requirements. © 2012 IEEE.

Research Area(s)

  • Cell image classification, convolutional neural network (CNN), field-programmable gate array (FPGA), hardware architecture, low-latency inference, multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM), quantized convolutional neural network (QCNN), reconfigurable computing

Citation Format(s)

Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network. / Wang, Maolin; Lee, Kelvin C. M.; Chung, Bob M. F. et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 7, 07.2022, p. 2853-2866.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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