title: Ð¡ÑÑÐ°ÑÐµÐ³Ð¸Ð¸ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð²ÐµÐºÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ° Ð¿Ð¾ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ 09.04.01 - ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ° Ð¸ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐµÑ Ð½Ð¸ÐºÐ° ; 09.04.01_17 - ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ (Ð¼ÐµÐ¶Ð´ÑÐ½Ð°ÑÐ¾Ð´Ð½Ð°Ñ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð°Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°)
titleType: Subtitle
Creators: name: ÐÑÐ°Ð·, ÐÐ°Ð±Ð°Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Advance Driving Assistance Systems
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð°Ð¼ÑÑÐ¸

description: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² ÑÐ²Ð»ÑÐµÑÑÑ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐ¼ Ð¿Ð¾Ð»ÐµÐ²ÑÐ¼ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸ÐµÐ¼ Advance Driving Assistance Systems (ADAS), ÐºÐ¾ÑÐ¾ÑÐ¾Ðµ ÑÑÐ°Ð»Ð¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÐ¼ Ð±Ð»Ð°Ð³Ð¾Ð´Ð°ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐµ Ð½Ð° Ð±Ð°Ð·Ðµ Ð²ÐµÐºÑÐ¾ÑÐ½ÑÑ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ¾Ð² VPRO Ð´Ð»Ñ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ ÑÑÐºÐ¾ÑÐ¸ÑÐµÐ»Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° Ð´Ð»Ñ ÐºÐ¾Ð½ÐºÑÐµÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ. ÐÑÐµÐ½Ñ Ð²Ð°Ð¶Ð½Ð¾ Ð¿Ð¾Ð»Ð½Ð¾ÑÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ñ, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼Ñ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»Ð¸Ð·Ð¼Ð°. Ð¡Ð²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ (CNN) ÑÐ²Ð»ÑÑÑÑÑ Ð¼Ð½Ð¾Ð³Ð¾Ð¾Ð±ÐµÑÐ°ÑÑÐ¸Ð¼ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð¼ Ð´Ð»Ñ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¼Ð°ÑÐºÐ¸ÑÐ¾Ð²ÐºÐ¸ ÑÑÐµÐ½, Ð½Ð¾ Ð¾Ð½Ð¸ ÑÐ°ÐºÐ¶Ðµ Ð¾ÑÐµÐ½Ñ Ð´Ð¾ÑÐ¾Ð³Ð¸ Ð² Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¼ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ð¸ Ð¸ ÑÑÐµÐ±ÑÑÑ Ð±Ð¾Ð»ÑÑÐ¾Ð³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹. ÐÐ³ÑÐ¾Ð¼Ð½Ð°Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ° Ð´Ð°Ð½Ð½ÑÑ ÑÐ¾Ð·Ð´Ð°ÐµÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ñ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð¹ Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑÑ, ÑÑÐ¾ Ð¾ÑÐµÐ½Ñ ÐºÑÐ¸ÑÐ¸ÑÐ½Ð¾ Ð´Ð»Ñ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¸ ÑÐ½ÐµÑÐ³Ð¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ. Ð­ÑÐ¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð¾ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð°Ð¼ÑÑÐ¸ Ð² VPRO, Ð¸Ð·ÑÑÐ°ÑÑÑÑ Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÑÐµÐ¼Ñ, ÑÑÐ¾Ð±Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ Ð¾Ð±ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÑÑ Ð¸Ð½ÑÐµÐ½ÑÐ¸Ð²Ð½ÑÐ¹ ÑÑÐ°ÑÐ¸Ðº Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¼Ð°ÑÐºÐ¸ÑÐ¾Ð²ÐºÐ¸ ÑÑÐµÐ½. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ CNN, Ð¸ Ð¾Ð½Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾Ð±ÐµÑÐ¿Ð¾ÐºÐ¾ÐµÐ½Ð° ÑÐµÐ¼, ÐºÐ°Ðº Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°ÑÑ ÑÑÐ¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð°Ð¼ÑÑÐ¸.
description: Object detection is a major field application of Advance Driving Assistance Systems (ADAS) which is made possible on VPRO- a vector processor based architecture for application specific AI-hardware accelerator. It is of great concern to utilize the processor fully by exploiting parallelism mechanisms. Convolutional Neural networks (CNNâs) are promising tool for intelligent scene labelling, but It is also computationally very expensive and requires lot of data transfers in order to perform calculations. Huge data transfers bring hardware bandwidth problem, it is very critical limiting speed and energy requirements. This study is about memory optimization in VPRO, different schemes are studied and compared to efficiently handle intensive data traffic for scene labelling. A suitable CNN model is suggested and it is also taken in concern how these memory optimizations can be implemented.