Title :
A 22.8GOPS 2.83mW neuro-fuzzy Object Detection Engine for fast multi-object recognition
Author :
Kim, Minsu ; Kim, Joo-Young ; Lee, Seungjin ; Oh, Jinwook ; Yoo, Hoi-Jun
Author_Institution :
Department of EECS, KAIST, Yuseong, Daejeon, Republic of Korea
Abstract :
A neuro-fuzzy Object Detection Engine (ODE) is proposed as the pre-processing accelerator of multi-object recognition processor to reduce the computational complexity. It performs a fast and robust neuro-fuzzy object detection algorithm with Motion Estimator (ME) and Visual Attention Engine (VAE) within 1ms. The mixed mode implementation achieves 22.9GOPS 2.83mW ODE, and reduces the area by 59% and power consumption by 44%. The ODE can increase the frame rate by 2.09x and reduce power consumption by 38% of the multi-object recognition processor.
Keywords :
Adaptive systems; Analog-digital conversion; Circuits; Decision making; Energy consumption; Engines; Motion estimation; Object detection; Object recognition; Robustness;
Conference_Titel :
VLSI Circuits, 2009 Symposium on
Conference_Location :
Kyoto, Japan
Print_ISBN :
978-1-4244-3307-0
Electronic_ISBN :
978-4-86348-001-8
