Face position detection by a convolutional neural network using an image filtering processor VLSI

Keisuke Korekado, Takashi Morie, Osamu Nomura, Teppei Nakano, Masakazu Matsugu, Atsushi Iwata

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Image filtering with large receptive-field area is essential for brain-like vision systems. The typical processing model using such filtering is convolutional neural networks (CoNNs). The CoNNs are a well-known robust image-recognition processing model, which imitates the vision nerve system in the brain. To realize such image processing, we have developed an image-filtering processor VLSI. The VLSI designed using a 0.35 μm CMOS process performs 6-bit precision convolutions for an image of 80 × 80 pixels with a receptive-field size of up to 51 × 51 pixels within 8.2 ms. Because the VLSI is based on a hybrid approach using pulse-width modulation (PWM) and digital circuits, low power-consumption of 220 mW has been achieved. Face position detection can be performed within 66 ms by using the developed VLSI.

Original languageEnglish
Pages (from-to)253-256
Number of pages4
JournalInternational Congress Series
Volume1291
DOIs
Publication statusPublished - 2006 Jun
Externally publishedYes

Keywords

  • Convolutional neural network
  • Face position detection
  • Image filtering
  • Merged/mixed analog-digital architecture

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of 'Face position detection by a convolutional neural network using an image filtering processor VLSI'. Together they form a unique fingerprint.

Cite this