scholarly journals Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System

2021 ◽  
Vol 15 ◽  
Author(s):  
Abderazek Ben Abdallah ◽  
Khanh N. Dang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Three Dimensional ◽  
Network On Chip ◽  
Spike Timing ◽  
Dimensional Structure ◽  
Spiking Neural Network ◽  
3D Ics ◽  
On Chip

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.

Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers

2012 ◽  
Vol 33 ◽  
pp. 42-57 ◽  
Author(s):  
Snaider Carrillo ◽  
Jim Harkin ◽  
Liam McDaid ◽  
Sandeep Pande ◽  
Seamus Cawley ◽  
...  
Keyword(s):  
Neural Network ◽  
Network On Chip ◽  
Spiking Neural Network ◽  
Adaptive Network ◽  
Hardware Implementations ◽  
Neural Network Hardware ◽  
On Chip

Scalable Hierarchical Network-on-Chip Architecture for Spiking Neural Network Hardware Implementations

2013 ◽  
Vol 24 (12) ◽  
pp. 2451-2461 ◽  
Author(s):  
Snaider Carrillo ◽  
Jim Harkin ◽  
Liam J. McDaid ◽  
Fearghal Morgan ◽  
Sandeep Pande ◽  
...  
Keyword(s):  
Neural Network ◽  
Network On Chip ◽  
Spiking Neural Network ◽  
Hierarchical Network ◽  
Hardware Implementations ◽  
Neural Network Hardware ◽  
On Chip

ACCURATE, ENERGY-EFFICIENT CLASSIFICATION WITH SPIKING RANDOM NEURAL NETWORK

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Khaled F. Hussain ◽  
Mohamed Yousef Bassyouni ◽  
Erol Gelenbe
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Processing ◽  
Large Scale ◽  
General Purpose ◽  
Spiking Neural Network ◽  
Random Neural Network ◽  
Computing Platforms ◽  
Industrial Adoption ◽  
Mean Time

Artificial Neural Networks (ANNs)-based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large-scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running the bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network, a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of being a spiking neural network. This is demonstrated on a number of real-world classification datasets.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

A new efficient multi‐task applications mapping for three‐dimensional Network‐on‐Chip based MPSoC

2021 ◽  
Author(s):  
Khadidja Gaffour ◽  
Mohammed Kamel Benhaoua ◽  
Abou El Hassan Benyamina ◽  
Amit Kumar Singh
Keyword(s):  
Three Dimensional ◽  
Network On Chip ◽  
Dimensional Network ◽  
On Chip
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