In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks that use binary values for activations and weights, instead of full precision values. With binary values, BNNs can execute computations using bitwise operations, which reduces execution time. Model sizes of BNNs are much smaller than their full precision counterparts. While the accuracy of a BNN model is generally less than full precision models, BNNs have been closing accuracy gap and are becoming more accurate on larger datasets like ImageNet. BNNs are also good candidates for deep learning implementations on FPGAs and ASICs due to their bitwise efficiency. We give a tutorial of the general BNN methodology and review various contributions, implementations and applications of BNNs.

Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.Electronics 2020, 9, 505 2 of 18 or sorting machines for food products on the market have been designed to automate these tasks and have proven to be very successful.Many recent developments were aimed at broad grading applications. For example, a machine learning-based system was built to use color and texture features for tomato grading and surface defect detection [1]. Color and texture features were collected for maturity evaluation, defect detection, size and shape analysis for mangoes [2]. Guava fruits were classified into four maturity levels using the K-nearest neighbor (KNN) algorithm to analyze color distribution in HSI (Hue, Saturation, and Intensity) color space [3]. Image processing techniques were used for corn seed grading [4], plum fruit maturity evaluation [5], quality assessment of pomegranate fruits [6], and the ripeness of palm fruit [7]. Two grading systems for date maturity [8] and skin quality [9] evaluation were designed specifically for Medjool dates. All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture fe...

This paper explores a set of learned convolutional kernels which we

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.

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