The brain mimicking Visual Attention Engine: An 80×60 digital Cellular Neural Network for rapid global feature extraction

Author

Lee, Seungjin ; Kim, Kwanho ; Kim, Minsu ; Kim, Joo-Young ; Yoo, Hoi-Jun

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., KAIST, Daejeon

fYear

2008

fDate

18-20 June 2008

Firstpage

26

Lastpage

27

Abstract

The visual attention engine (VAE), an 80 times 60 digital cellular neural network, rapidly extracts global features used as attentional cues to streamline detailed object recognition. A peak performance of 24 GOPS is achieved by 120 processing elements (PE) shared by the cells. 2D shift register based data transactions enable 93% PE utilization. Integrated within an object recognition SoC, the 4.5 mm² VAE running at 200 MHz improves object recognition frame rate by 83% while consuming just 84 mW.

Keywords

feature extraction; neural nets; object recognition; shift registers; system-on-chip; 2D shift register; GOPS; SoC; brain mimicking visual attention engine; digital cellular neural network; frequency 200 MHz; object recognition; power 84 mW; processing elements; rapid global feature extraction; Aerodynamics; Cellular neural networks; Data mining; Engines; Feature extraction; Filters; Object recognition; Routing; Shift registers; Wire;

fLanguage

English

Publisher

ieee

Conference_Titel

VLSI Circuits, 2008 IEEE Symposium on

Conference_Location

Honolulu, HI

Print_ISBN

978-1-4244-1804-6

Electronic_ISBN

978-1-4244-1805-3

Type

conf

DOI

10.1109/VLSIC.2008.4585938

Filename

4585938