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Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices.

Manas Ranjan Biswal1, Tahesin Samira Delwar1, Abrar Siddique1,2

  • 1Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea.

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Summary
This summary is machine-generated.

Quantized neural networks (QNNs) and binarized neural networks (BNNs) offer improved performance for Internet of Things (IoT) devices compared to conventional convolution neural networks (CNNs). FPGA implementation demonstrates QNNs and BNNs provide efficient, low-power pattern recognition for IoT applications.

Keywords:
FPGAbinary neural network (BNN)convolutional neural network (CNN)internet of things (IoT)pattern recognitionquantized neural network (QNN)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • The proliferation of the Internet of Things (IoT) necessitates efficient computational models.
  • Conventional Convolutional Neural Networks (CNNs) face limitations in memory access costs and computational efficiency for resource-constrained IoT devices.
  • Quantized Neural Networks (QNNs), including Binarized Neural Networks (BNNs), offer a promising alternative for enhanced performance.

Purpose of the Study:

  • To implement and analyze Convolutional Neural Networks (CNNs), Quantized Neural Networks (QNNs), and Binarized Neural Networks (BNNs) for pattern recognition on Field-Programmable Gate Arrays (FPGAs).
  • To evaluate the performance of these models as IoT devices, focusing on accuracy, weight bit error, Receiver Operating Characteristic (RoC) curves, and execution speed.
  • To explore optimization techniques for CNN and QNN models within an FPGA framework.

Main Methods:

  • Implementation of CNN, QNN, and Binarized Neural Network (BNN) based pattern recognition on a Xilinx Zynq 7020 series Pynq Z2 FPGA board, serving as an IoT device.
  • Comparative analysis based on accuracy, weight bit error, RoC curve, and execution speed using MNIST and CIFAR-10 datasets.
  • Discussion of optimization strategies for CNN and QNN models.

Main Results:

  • Full precision (32-bit) models achieved 95.5% accuracy on MNIST and 79.22% on CIFAR-10, with execution times of 5.8 ms and 18 ms, respectively.
  • QNNs and BNNs are identified as offering better performance in terms of low power and resource utilization on hardware platforms.
  • FPGA implementation demonstrates the potential of QNN-enabled IoT devices for reduced latency and power consumption.

Conclusions:

  • QNNs and BNNs present a viable solution for efficient pattern recognition in IoT applications, outperforming traditional CNNs in resource-constrained environments.
  • FPGA-based implementation of QNNs and BNNs can significantly reduce processing latency and power consumption, enabling advanced functionalities in IoT devices.
  • Further research into optimization techniques can further enhance the capabilities of QNNs and BNNs for diverse IoT applications.