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

Clicks: 283
ID: 267945
2021
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Abstract
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.
Reference Key
simons2021electronicsefficient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Taylor Simons;Dah-Jye Lee;Simons, Taylor;Lee, Dah-Jye;
Journal Electronics
Year 2021
DOI
10.3390/electronics10131511
URL
https://www.mdpi.com/2079-9292/10/13/1511/htm
Keywords
image classification cnn asics visual inspection fpgas bnn

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