Managing a large volume of coins poses a complex challenge in various fields, particularly within Tunisian administrations, specifically in terms of counting and classification. In this case, several coin recognition ...
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Following the remarkable performance demonstrated by the Transformer architecture in the field of computer vision as well as natural language processing (NLP), there is a growing demand for embeddedsystems capable of...
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ISBN:
(纸本)9798350380415;9798350380408
Following the remarkable performance demonstrated by the Transformer architecture in the field of computer vision as well as natural language processing (NLP), there is a growing demand for embeddedsystems capable of executing Vision Transformer (ViT) applications as well as Convolutional Neural Network (CNN) applications efficiently. Since CNN accelerators are already widely used commercially, this paper explores the possibility of using existing CNN accelerators to support ViT rather than developing separate accelerators for each. CNN accelerators inherently have some limitations in efficiently handling operations in transformers: matrix multiplication (MM) operations with two non-constant matrices and nonlinear operations. To overcome these limitations, we first propose a novel technique to efficiently handle MM operations without special reshaping hardware in an adder-tree type CNN accelerator. And we propose an optimal scheduling method to minimize the idle time caused by offloading computation of nonlinear operations of the Transformer. Additionally, we investigate the possibility of executing layer normalization and GELU operations on the accelerator with minor extensions. The experimental results validate the effectiveness of the proposed methods.
This paper presents an innovative edge-based fruit ripeness detection system that leverages ambient sensor data and machine learning algorithms to accurately classify fruit ripeness in real-time. The proposed system u...
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Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. For this, it is crucial that a machine...
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ISBN:
(纸本)9781665488679
Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. For this, it is crucial that a machine learning system can be trained to reproduce the target dynamics sufficiently well. On the example of forcing a chaotic parametrization of the Lorenz system into intermittent dynamics, we show first that classical reservoir computing excels at this task. In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead. It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available. This opens even further practical control applications in real world problems where data is restricted.
The incorporation of the Internet based Things (IoT) into medical applications has significantly improved healthcare operations and patient treatment. real-time patient monitoring systems, coupled with remote diagnost...
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Power electronic systems are essential in modern electrical engineering, playing a critical role in various applications. Designing and developing hardware prototypes for these systems presents significant challenges ...
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The integration of 5G and Internet of Vehicles (IOV) technologies will quicken intelligent transportation system developments, improving urban mobility and safety. This systematic literature review paper summarizes th...
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In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-timeapplications. This paper presents YOLOv8, a novel obje...
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Low-cost IoT sensor systems and real-time remote monitoring are rapidly evolving technologies with immense potential for various applications. This paper explores the design, features, and benefits of low-cost IoT sen...
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The wavelet transform is commonly used as a feature extraction method for brain signal decoding, but extracting this type of feature has a high energy cost as the wavelet transform relies on computing a convolution op...
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