scholarly journals A GPU-accelerated image reduction pipeline

2020 ◽  
Author(s):  
Masafumi Niwano ◽  
Katsuhiro L Murata ◽  
Ryo Adachi ◽  
Sili Wang ◽  
Yutaro Tachibana ◽  
...  
Keyword(s):  
Image Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Emission Measure ◽  
Robotic Telescope ◽  
Graphics Processing ◽  
High Speed Image Processing ◽  
Python Package ◽  
Telescope System

Abstract We developed a high-speed image reduction pipeline using Graphics Processing Units (GPUs) as hardware accelerators. Astronomers desire to detect the emission measure counterpart of gravitational-wave sources as soon as possible and to share in the systematic follow-up observation. Therefore, high-speed image processing is important. We developed a new image-reduction pipeline for our robotic telescope system, which uses a GPU via the Python package CuPy for high-speed image processing. As a result, the new pipeline has increased in processing speed by more than 40 times compared with the current one, while maintaining the same functions.

scholarly journals Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4097-4108 ◽  
Author(s):  
Moustafa Ahmed ◽  
Yas Al-Hadeethi ◽  
Ahmed Bakry ◽  
Hamed Dalir ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Graphics Processing Units ◽  
High Speed ◽  
Fourier Transforms ◽  
Optoelectronic Device ◽  
Small Sample ◽  
Sample Number ◽  
Chip Area ◽  
Domain Specific ◽  
Graphics Processing

AbstractThe technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.

scholarly journals CLIJ: GPU-accelerated image processing for everyone

2019 ◽  
Author(s):  
Robert Haase ◽  
Loic A. Royer ◽  
Peter Steinbach ◽  
Deborah Schmidt ◽  
Alexandr Dibrov ◽  
...  
Keyword(s):  
Image Processing ◽  
Graphics Processing Units ◽  
End Users ◽  
Entry Level ◽  
Speed Up ◽  
Mobile Computers ◽  
Graphics Processing

AbstractGraphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.

High-speed image processing on digital smart pixel array

2002 ◽  
Vol 38 (12) ◽  
pp. 590 ◽  
Author(s):  
H. Kawai ◽  
A. Baba ◽  
M. Shibata ◽  
Y. Takeuchi ◽  
T. Komuro ◽  
...  
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Pixel Array ◽  
Smart Pixel ◽  
High Speed Image Processing ◽  
Smart Pixel Array

Real-time lock-in imaging by a newly developed high-speed image-processing charge coupled device video camera

2003 ◽  
Vol 74 (3) ◽  
pp. 1393-1396 ◽  
Author(s):  
Kentarou Nishikata ◽  
Yoshihide Kimura ◽  
Yoshizo Takai ◽  
Takashi Ikuta ◽  
Ryuichi Shimizu
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Speed ◽  
Video Camera ◽  
Charge Coupled Device ◽  
Lock In ◽  
High Speed Image Processing
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