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ieeexplore.ieee.org article

Neural Network Accelerator Architecture Designed for Next Generation Communication Systems | IEEE Conference Publication | IEEE Xplore

https://ieeexplore.ieee.org/document/10941954/

Neural Network Accelerator Architecture Designed for Next Generation Communication Systems | IEEE Conference Publication | IEEE Xplore. # Neural Network Accelerator Architecture Designed for Next Generation Communication Systems. In the future 6G wireless communication networks, the artificial intelligent (AI) technology is a promising way to fundamentally reconstruct the future communication syst...Show More. In the future 6G wireless communication networks, the artificial intelligent (AI) technology is a promising way to fundamentally reconstruct the future communication system. Aiming to implement the existing deep learning algorithms for wireless communication more efficiently, in this paper, we propose an novel AI accelerator architecture, which is driven by data streams and separates computing and control. An affinity compiler and a supporting emulator are designed for this architecture, which meets most of the neural network inference acceleration requirements. Simulation results show that various neural network for wireless communication can be accelerated efficiently. **Published in:** 2024 10th International Conference on Computer and Communications (ICCC).

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coursera.org article

4 Types of Neural Network Architecture | Coursera

https://www.coursera.org/articles/neural-network-architecture

The four main types of neural network architecture are feedforward, recurrent, convolutional, and generative adversarial. Discover different types of neural network architectures and careers you can pursue to work with these AI algorithms. The arrangement of these nodes and layers makes up the neural network's architecture. ## What is a neural network architecture? ### What are the layers of a neural network architecture? #### How does a feedforward neural network architecture work? #### How does a recurrent neural network architecture work? In addition to the architecture found in the feedforward neural network, a recurrent network uses loops to circle the data back through the hidden layers before returning an output. The basic architecture of a generative adversarial network is two distinct neural networks working in tandem to produce an output from the input. ## How to get started in neural network architecture. If you are interested in a career in neural network architecture, three potential careers to consider are test engineer, research scientist, and applied scientist.

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brain-ca.com article

Why the Future of AI Might Not Be Neural Networks

https://brain-ca.com/why-the-future-of-ai-might-not-be-neural-networks/

Researchers around the world are exploring non-neural AI architectures that can break this dependence on heavy computation and endless training.

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en.wikipedia.org article

Neural network (machine learning) - Wikipedia

https://en.wikipedia.org/wiki/Neural_network_(machine_learning)

Architectural innovations such as convolutional neural networks (CNNs) significantly improved performance in computer vision tasks, while recurrent neural

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leewayhertz.com article

Neural networks: Architecture, applications, case studies, development and implementation

https://www.leewayhertz.com/what-are-neural-networks/

Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge. By emulating the behavior of neurons and their interconnections, neural networks can learn from data, recognize patterns, and make intelligent decisions, contributing to the field of artificial intelligence. This can be achieved by representing each image as a flattened array of pixel values or by utilizing more advanced techniques, such as Convolutional Neural Networks (CNNs) that can directly process image data. Deep learning models are characterized by having multiple hidden layers (referred to as deep neural networks) between the input and output layers. To summarize, neural networks are a broad class of algorithms inspired by the brain, while deep learning is a specific area of machine learning that focuses on training deep neural networks with multiple layers to learn hierarchical representations of data. Deep learning is a powerful technique within the broader context of neural networks, enabling the development of highly advanced AI models.

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medium.com article

Neural Network Architectures - by Sefa Bilicier - Medium

https://medium.com/data-science-collective/neural-network-architectures-adf8f…

As you build your own models, remember: these architectures are not competing alternatives — they are complementary tools in a growing toolkit.

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