Title :
Hardware accelerated convolutional neural networks for synthetic vision systems
Author :
Farabet, Clément ; Martini, Berin ; Akselrod, Polina ; Talay, Selçuk ; LeCun, Yann ; Culurciello, Eugenio
Author_Institution :
Center for Neural Sci., New York Univ., New York, NY, USA
fDate :
May 30 2010-June 2 2010
Abstract :
In this paper we present a scalable hardware architecture to implement large-scale convolutional neural networks and state-of-the-art multi-layered artificial vision systems. This system is fully digital and is a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images. We present a performance comparison between a software, FPGA and ASIC implementation that shows a speed up in custom hardware implementations.
Keywords :
application specific integrated circuits; computer vision; field programmable gate arrays; image recognition; image segmentation; neural nets; ASIC; FPGA; convolutional neural network; megapixel image; real time detection; real time recognition; real time segmentation; scalable hardware architecture; state-of-the-art multilayered artificial vision system; vision engine; Acceleration; Artificial neural networks; Computer architecture; Engines; Large-scale systems; Machine vision; Multi-layer neural network; Neural network hardware; Neural networks; Real time systems;
Conference_Titel :
Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-5308-5
Electronic_ISBN :
978-1-4244-5309-2
DOI :
10.1109/ISCAS.2010.5537908
