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Partitioning multi-layer edge network for neural network collaborative computing
There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device...
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Neural architecture search for in-memory computing-based deep learning accelerators
The rapid growth of artificial intelligence and the increasing complexity of neural network models are driving demand for efficient hardware...
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Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements, the...
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Reconfigurable spatial-parallel stochastic computing for accelerating sparse convolutional neural networks
Edge devices play an increasingly important role in the convolutional neural network (CNN) inference. However, the large computation and storage...
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Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks
Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across...
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Leveraging Quantum computing for synthetic image generation and recognition with Generative Adversarial Networks and Convolutional Neural Networks
The generation and classification of synthetic images is a challenging and important task in the digital age. Generative Adversarial Networks are...
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Discontinuity Computing Using Physics-Informed Neural Networks
Simulating discontinuities has been a long-standing challenge, especially when dealing with shock waves characterized by strong nonlinear features....
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Silicon carbide nanowire-based multifunctional and efficient visual synaptic devices for wireless transmission and neural network computing
With the rapid development of big data and the internet of things, the current computing paradigms based on traditional Von Neumann architecture have...
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Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks
With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have...
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Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of...
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Exploring the distributed learning on federated learning and cluster computing via convolutional neural networks
Distributed learning has led to the development of federated learning and cluster computing; however, the two methods are very different. Therefore,...
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On Fast Computing of Neural Networks Using Central Processing Units
AbstractThis work is devoted to methods for creating fast and accurate neural network algorithms for central processors, which were proposed by...
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Accelerating neural network architecture search using multi-GPU high-performance computing
Neural networks stand out from artificial intelligence because they can complete challenging tasks, such as image classification. However, designing...
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Stochastic Computing Applications to Artificial Neural Networks
Stochastic computing (SC) has gained popularity for the creation of energy-efficient artificial neural networks (ANNs). For instance, by using...
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Dependent Task Scheduling Using Parallel Deep Neural Networks in Mobile Edge Computing
Conventional detection techniques aimed at intelligent devices rely primarily on deep learning algorithms, which, despite their high precision, are...
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Robot knowledge analysis based on cognitive computing and modular neural network feature combination
With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important...
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Memristive patch attention neural network for facial expression recognition and edge computing
Facial expression recognition has made a significant progress as a result of the advent of more and more convolutional neural networks (CNN)....
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Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a...
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Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits...
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Nonlinear Dynamics and Computing in Recurrent Neural Networks
Nonlinearity is a key concept in the design and implementation of photonic neural networks for computing. This chapter introduces the fundamental...