scholarly journals A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6006
Author(s):  
Ling Zhang ◽  
Jing Yang ◽  
Cong Shi ◽  
Yingcheng Lin ◽  
Wei He ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Network Inference ◽  
Sensory Information ◽  
Vlsi Architecture ◽  
Hardware Architecture ◽  
Time Step ◽  
Neuromorphic Hardware ◽  
Cost Efficient ◽  
Embedded Applications

Neuromorphic hardware systems have been gaining ever-increasing focus in many embedded applications as they use a brain-inspired, energy-efficient spiking neural network (SNN) model that closely mimics the human cortex mechanism by communicating and processing sensory information via spatiotemporally sparse spikes. In this paper, we fully leverage the characteristics of spiking convolution neural network (SCNN), and propose a scalable, cost-efficient, and high-speed VLSI architecture to accelerate deep SCNN inference for real-time low-cost embedded scenarios. We leverage the snapshot of binary spike maps at each time-step, to decompose the SCNN operations into a series of regular and simple time-step CNN-like processing to reduce hardware resource consumption. Moreover, our hardware architecture achieves high throughput by employing a pixel stream processing mechanism and fine-grained data pipelines. Our Zynq-7045 FPGA prototype reached a high processing speed of 1250 frames/s and high recognition accuracies on the MNIST and Fashion-MNIST image datasets, demonstrating the plausibility of our SCNN hardware architecture for many embedded applications.

A Low-Cost High-Speed Neuromorphic Hardware Based on Spiking Neural Network

2019 ◽  
Vol 66 (9) ◽  
pp. 1582-1586 ◽  
Author(s):  
Edris Zaman Farsa ◽  
Arash Ahmadi ◽  
Mohammad Ali Maleki ◽  
Morteza Gholami ◽  
Hima Nikafshan Rad
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Low Cost ◽  
Spiking Neural Network ◽  
Neuromorphic Hardware

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

Erratum: High speed opto-electronic neural network

1990 ◽  
Vol 26 (20) ◽  
pp. 1739
Author(s):  
N.M. Barnes ◽  
P. Healey ◽  
P. McKee ◽  
A.W. O'Neill ◽  
M.A.Z. Rejmangreene ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed

scholarly journals A Critical Review of Supersonic Flow Control for High-Speed Applications

2021 ◽  
Vol 11 (15) ◽  
pp. 6899
Author(s):  
Abdul Aabid ◽  
Sher Afghan Khan ◽  
Muneer Baig
Keyword(s):  
Supersonic Flow ◽  
Flow Control ◽  
Active Control ◽  
Critical Review ◽  
High Speed ◽  
Passive Control ◽  
Control Method ◽  
Sudden Expansion ◽  
Control Approach ◽  
Cost Efficient

In high-speed fluid dynamics, base pressure controls find many engineering applications, such as in the automobile and defense industries. Several studies have been reported on flow control with sudden expansion duct. Passive control was found to be more beneficial in the last four decades and is used in devices such as cavities, ribs, aerospikes, etc., but these need additional control mechanics and objects to control the flow. Therefore, in the last two decades, the active control method has been used via a microjet controller at the base region of the suddenly expanded duct of the convergent–divergent (CD) nozzle to control the flow, which was found to be a cost-efficient and energy-saving method. Hence, in this paper, a systemic literature review is conducted to investigate the research gap by reviewing the exhaustive work on the active control of high-speed aerodynamic flows from the nozzle as the major focus. Additionally, a basic idea about the nozzle and its configuration is discussed, and the passive control method for the control of flow, jet and noise are represented in order to investigate the existing contributions in supersonic speed applications. A critical review of the last two decades considering the challenges and limitations in this field is expressed. As a contribution, some major and minor gaps are introduced, and we plot the research trends in this field. As a result, this review can serve as guidance and an opportunity for scholars who want to use an active control approach via microjets for supersonic flow problems.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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