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Article

Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electrical and Computer Engineering Department, Brigham Young University, Provo, UT 84602, USA
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Author to whom correspondence should be addressed.
Academic Editor: Gwanggil Jeon
Electronics 2021, 10(13), 1511; https://doi.org/10.3390/electronics10131511
Received: 28 May 2021 / Revised: 15 June 2021 / Accepted: 17 June 2021 / Published: 23 June 2021
There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models. View Full-Text
Keywords: image classification; CNN; BNN; FPGAs; ASICs; visual inspection image classification; CNN; BNN; FPGAs; ASICs; visual inspection
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MDPI and ACS Style

Simons, T.; Lee, D.-J. Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs. Electronics 2021, 10, 1511. https://doi.org/10.3390/electronics10131511

AMA Style

Simons T, Lee D-J. Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs. Electronics. 2021; 10(13):1511. https://doi.org/10.3390/electronics10131511

Chicago/Turabian Style

Simons, Taylor, and Dah-Jye Lee. 2021. "Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs" Electronics 10, no. 13: 1511. https://doi.org/10.3390/electronics10131511

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