Computation methods using custom circuits are frequently employed to improve the throughput and power efficiency of computing systems. Hardware development, however, can incur significant development costs because designs at the register-transfer level (RTL) with a hardware description language (HDL) are time-consuming. This paper proposes a hardware and software co-design environment, named Mulvery, which is designed for non-professional hardware designer We focus on the similarities between functional reactive programming (FRP) and dataflow in computation. This study provides an idea to design hardware with a dynamic typing language, such as Ruby, using FRP and provides the proofof-concept of the method. Mulvery, which is a hardware and software codesign tool based on our method, reduces development costs. Mulvery exhibited high performance compared with software processing techniques not equipped with hardware knowledge. According to the experiment, the method allows us to design hardware without degradation of performance. The sample application applied a Laplacian filter to an image with a size of 128 × 128 and processed a convolution operation within one clock.
In recent years, Japan’s agricultural industry has faced a number of challenges, including a decline in production due to a decrease in farmland area, a shortage of labor due to a decrease in the number of producers, and an aging population. Therefore, in recent years, smart agriculture using robots and IoT has been studied. A caliper is often used to analyze the growth of tomatoes in a plant factory, but this method may damage the stems and is also hard on the measurer. We developed a system that detects them through image analysis and measures the thickness of stems and the length between flower clusters and growing points. The camera device developed in this study costs about USD 150 and once installed, it does not need to be moved unless it malfunctions. The camera device reduces the effort required to analyze crop growth by about 80%.
In training-based Machine Learning applications, the training data are frequently labeled by non-experts and expose substantial label noise which greatly alters the training models. In this work, a novel method for reducing the effect of label noise is introduced. The rules are created from expert knowledge to identify the incorrect non-expert training data. Using the gradient descent algorithm, the violating data samples are weighted less to mitigate their effects during model training. The proposed method is applied to the image classification problem using Manga109 and CIFAR-10 dataset. The experiments show that when the noise level is up to 50% our proposed method significantly increases the accuracy of the model compared to conventional learning methods.
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