НЕЙРОМОРФНЫЙ ПРОЦЕССОР «АЛТАЙ» ДЛЯ ЭНЕРГОЭФФЕКТИВНЫХ ВЫЧИСЛЕНИЙ

Предложена масштабируемая нейроморфная архитектура для исполнения импульсных нейронных сетей, разработан прототип СБИС с данной архитектурой. Проведены оценки энергопотребления проекта прототипа СБИС при распознавании тестовых изображений из наборов MNIST и CIFAR-10. The paper presents a scalable neuromorphic architecture for spiking neural network inference. A VLSI prototype based on this architecture has been developed. The energy consumption of the VLSI prototype project was estimated during a test image recognition from the MNIST and CIFAR-10 sets.

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TripleBrain: An Edge Neuromorphic Architecture for High-accuracy Single-layer Spiking Neural Network with On-chip Self-organizing and Reinforcement Learning

10.1109/icta53157.2021.9661702 ◽

2021 ◽

Author(s):

Haibing Wang ◽

Zhen He ◽

Jinsong Rao ◽

Tengxiao Wang ◽

Junxian He ◽

...

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Single Layer ◽

High Accuracy ◽

Spiking Neural Network ◽

On Chip ◽

Neuromorphic Architecture ◽

Self Organizing

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A field-programmable gate array system for sonar image recognition based on convolutional neural network

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820939345 ◽

2020 ◽

pp. 095965182093934

Author(s):

Chong Wang ◽

Yu Jiang ◽

Kai Wang ◽

Fenglin Wei

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Convolutional Neural Network ◽

Image Recognition ◽

Field Programmable Gate Array ◽

Oil And Gas ◽

Sonar Image ◽

Field Programmable ◽

Sonar Images ◽

Gate Array

Subsea pipeline is the safest, most reliable, and most economical way to transport oil and gas from an offshore platform to an onshore terminal. However, the pipelines may rupture under the harsh working environment, causing oil and gas leakage. This calls for a proper device and method to detect the state of subsea pipelines in a timely and precise manner. The autonomous underwater vehicle carrying side-scan sonar offers a desirable way for target detection in the complex environment under the sea. As a result, this article combines the field-programmable gate array, featuring high throughput, low energy consumption and a high degree of parallelism, and the convolutional neural network into a sonar image recognition system. First, a training set was constructed by screening and splitting the sonar images collected by sensors, and labeled one by one. Next, the convolutional neural network model was trained by the set on the workstation platform. The trained model was integrated into the field-programmable gate array system and applied to recognize actual datasets. The recognition results were compared with those of the workstation platform. The comparison shows that the computational precision of the designed field-programmable gate array system based on convolutional neural network is equivalent to that of the workstation platform; however, the recognition time of the designed system can be saved by more than 77%, and its energy consumption can also be saved by more than 96.67%. Therefore, our system basically satisfies our demand for energy-efficient, real-time, and accurate recognition of sonar images.

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SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM

10.1109/dac18074.2021.9586332 ◽

2021 ◽

Author(s):

Rachmad Vidya Wicaksana Putra ◽

Muhammad Abdullah Hanif ◽

Muhammad Shafique

Keyword(s):

Neural Network ◽

Energy Efficient ◽

Network Inference ◽

Spiking Neural Network

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A convolutional neural network accelerator for real-time underwater image recognition of autonomous underwater vehicle

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820958208 ◽

2020 ◽

pp. 095965182095820

Author(s):

Wanting Zhao ◽

Hong Qi ◽

Yu Jiang ◽

Chong Wang ◽

Fenglin Wei

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Convolutional Neural Network ◽

Real Time ◽

Image Recognition ◽

High Performance ◽

Autonomous Underwater Vehicle ◽

Random Access ◽

Underwater Vehicle ◽

Underwater Image

In the field of underwater image recognition, a chip with smaller footprint and lower energy consumption is required to be implanted into autonomous intelligent underwater vehicle to make real-time response to the surrounding objects. Therefore, a promising accelerator with high performance and low energy consumption is designed, which optimizes the features possessed by convolutional neural network. The sharing of weights between neurons reduces the memory requirement. With all convolutional neural network data stored within on-chip static random-access memory, the need for memory access is drastically decreased. Besides, several small processing elements are used to form neural functional unit, which considerably reduces the bandwidth requirement through inter-processing element data transmission. By sending control signals to autonomous underwater vehicle, this accelerator enables it to avoid dangerous areas such as rocks and algae in time. The result suggests the proposed accelerator successfully achieves a higher processing speed than that of CPU and GPU with a footprint of 6.09 mm2 only and the energy consumption of 327.3 mW at 1 GHz.

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Fast recognition method of moving video images based on BP neural networks

Open Physics ◽

10.1515/phys-2018-0123 ◽

2018 ◽

Vol 16 (1) ◽

pp. 1024-1032 ◽

Cited By ~ 1

Author(s):

Yu Shao ◽

Deden Witarsyah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Energy Consumption ◽

Fault Tolerance ◽

Real Time ◽

Image Recognition ◽

Bp Neural Network ◽

Image Features ◽

Video Image ◽

Error Signal

Abstract At present, the accuracy of real-time moving video image recognition methods are poor. Also energy consumption is high and fault tolerance is not ideal. Consequently this paper proposes a method of moving video image recognition based on BP neural networks. The moving video image is divided into two parts: the key content and the background by binary gray image. By collecting training cubes. The D-SFA algorithm is used to extract moving video image features and to construct feature representation. The image features are extracted by collecting training cubes. The BP neural network is constructed to get the error function. The error signal is returned continuously along the original path. By modifying the weights of neurons in each layer, the weights propagate to the input layer step by step, and then propagates forward. The two processes are repeated to minimize the error signal. The result of image feature extraction is regarded as the input of BP neural network, and the result of moving video image recognition is output. And fault tolerance in real-time is better than the current method. Also the recognition energy consumption is low, and our method is more practical.

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