A 5.28-mm² 4.5-pJ/SOP Energy-Efficient Spiking Neural Network Hardware With Reconfigurable High Processing Speed Neuron Core and Congestion-Aware Router

2021 ◽  
pp. 1-14
Author(s):  
Junran Pu ◽  
Wang Ling Goh ◽  
Vishnu P. Nambiar ◽  
Ming Ming Wong ◽  
Anh Tuan Do
Keyword(s):  
Neural Network ◽  
Processing Speed ◽  
Energy Efficient ◽  
Spiking Neural Network ◽  
High Processing Speed ◽  
Neural Network Hardware ◽  
High Processing ◽  
Congestion Aware

Wafer‐Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy‐Efficient Neural Network Hardware

2021 ◽  
pp. 2103376 ◽  
Author(s):  
Sifan Li ◽  
Mei‐Er Pam ◽  
Yesheng Li ◽  
Li Chen ◽  
Yu‐Chieh Chien ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Wafer Scale ◽  
Crossbar Array ◽  
Neural Network Hardware ◽  
Memristor Crossbar

scholarly journals An Analysis of the Optical System of a Length Measurement Comparator

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 518-525
Author(s):  
Rimantas LAZDINAS ◽  
Mindaugas JUREVICIUS ◽  
Vladas VEKTERIS ◽  
Vytautas TURLA ◽  
Eugenijus JURKONIS
Keyword(s):  
Optical System ◽  
Processing Speed ◽  
Length Measurement ◽  
Identification Algorithm ◽  
High Processing Speed ◽  
Image Blurring ◽  
Scale Calibration ◽  
Calibration Accuracy ◽  
The Impact ◽  
High Processing

In the paper, the optical system of a precise bar length measuring comparator is analysed. The requirements for such a system are determined and systematized. The impact of the resolution, signal discretization frequency, image blurring, bar edge measurement indeterminacy and camera noise on measuring the bar width and establishing the bar position is discussed upon. Various algorithms have been analysed and finally a bar edge identification algorithm oriented to the scale calibration accuracy and the high processing speed was proposed. In the end of the paper, conclusions are provided.

scholarly journals Area- and Energy-Efficient STDP Learning Algorithm for Spiking Neural Network SoC

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 216922-216932
Author(s):  
Giseok Kim ◽  
Kiryong Kim ◽  
Sara Choi ◽  
Hyo Jung Jang ◽  
Seong-Ook Jung
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Learning Algorithm ◽  
Spiking Neural Network

Flour Bag Counting System Based on Machine Vision

2012 ◽  
Vol 562-564 ◽  
pp. 1625-1629 ◽  
Author(s):  
Chun Xia Ma ◽  
Wen He Song ◽  
Jun Wu ◽  
Yue Wang ◽  
Zhi Tao Xiao
Keyword(s):  
Processing Speed ◽  
Counting Process ◽  
Plant Production ◽  
Detection Accuracy ◽  
Test Results ◽  
Counting System ◽  
High Processing Speed ◽  
Flour Production ◽  
High Processing ◽  
Time Acquisition

According to the specific characteristics of Flour Plant production line, the bag counting system for Flour Plant based on Daheng Image acquisition card DH-CG410 and Microsoft VC++ 2005 platform is designed. Using digital image processing technology, the system can be used to detect flour bag by analyzing real-time acquisition video of the flour bag. So it is available for flour production counting. Test results show that the system has the advantages of stable working state, high detection accuracy, high processing speed. It can systematize and standardize the Flour Plant flour bag counting process.

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