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

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)253-256
ページ数4
ジャーナルInternational Congress Series
1291
DOI
出版ステータスPublished - 2006 6
外部発表はい

ASJC Scopus subject areas

  • 医学(全般)

フィンガープリント

「Face position detection by a convolutional neural network using an image filtering processor VLSI」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル