IEEE Micro 2012.

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19 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

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“Low
-
Power, Real
-
Time Object
-
Recognition Processors for
Mobile Vision Systems”,

IEEE Micro 2012.

Jinwook

Oh ;
Gyeonghoon

Kim ;
Injoon

Hong ;
Junyoung

Park ;
Seungjin

Lee ;
Joo
-
Young Kim ;
Jeong
-
Ho Woo ; Hoi
-
Jun
Yoo

Presenter:
Juseong

Lee, 2013021037

1

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



2

Object Recognition by
Juseong

Lee

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



3

Object Recognition by
Juseong

Lee

Introduction

4

Source by MBN News

Introduction

5


Object recognition system


Require real
-
time operation


High performance


Low power in mobile system



How can implement
?


Find suitable algorithm


SIFT algorithm


Hardware
optimization


Algorithm
optimization


Make exclusive
processor


Parallel computation


Multi
-
threading


NoC



SIFT
-

S
cale
I
nvariant
F
eature
T
ransform

NoC

-

N
etwork
o
n
C
hip

Source by VOLVO

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



6

Object Recognition by
Juseong

Lee

Background Knowledge

7


What is SIFT algorithm?


Scale
I
nvariant
F
eature
T
ransform


The most popular candidate


For how to extract some interest points out of the object and describe them



Robust against changes in translation, scaling, and rotation.


Image matching by
SIFT

Background Knowledge

8


What’s the problem in SIFT
-
based object recognition?


Consumes a lot of power


Owing to the heavy computation required in descriptor Gen. and matching


Today’s high
-
resolution image sensors & tight power budgets


Make real
-
time SIFT implementation in mobile device even harder


Scare resources
problem

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



9

Object Recognition by
Juseong

Lee

Main Idea

10


How can we solve the problem?


Make
an object
-
recognition
processor


Using an attention
-
based recognition algorithm


For energy efficiency


A heterogeneous multicore architecture


For data and thread parallelism


Network
-
on
-
Chip(
NoC
) communication


For high bandwidth



The processor determines
R
egions
o
f
I
nterest(ROI) part of image


For minimizing unnecessary computations



Heterogeneous multicore architecture


provides several types of parallelism


achieves high throughput


low power consumption



H
igh
-
bandwidth
NoC

plays a role as the communications backbone

Why find ROI?

11


Image processing algorithm has no regard throughput

Image size

480 x 360

Objects have feature!

172,800 computations!

Example) Edge detection

You can select part for reducing computation!

Main Idea


BONE V

12

Using Conventional method

Using Main Idea

Main Idea


Algorithm

13


Attention
-
based object recognition

Main Idea


Architecture

14

Pixel level parallel

Very long instruction word

3 stage task level pipeline

1.5x


power consumption

5 stage fine
-
grained pipeline

3.45x


pipeline throughput

SMT
-
enabled heterogeneous
multicore processor

15


Throughput
-
optimized SFEC


Find ROI tile for energy efficiency


Memory locality with high bandwidth utilization



Latency
-
optimized FMP


ROI tile and
NoC

help latency



Power
-
optimized MLE


Changes the core’s thread allocation


and operating voltage and frequency dynamically


BONE
-
V5:

SFEC:

S
MT
-
enabled
F
eature
E
xtraction
C
luster

FMP:

F
eature
M
atching
P
rocessor

MLE:

M
achine
L
earning
E
ngine

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



16

Object Recognition by
Juseong

Lee

Implementation

17

Implementation
-

Comparing

18

19

Implementation
-

Comparing

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



20

Object Recognition by
Juseong

Lee

Conclusion


Energy efficient system is important to
improve performance



Algorithm
and architecture have to optimize at
the same
time



BONE
-
V multicore processors can apply real
-
time object recognition system



Future BONE
-
V processors will
further lower
the power consumption.


21

Outline


Introduction



Background



Main Idea



Implementation



Conclusion



Evaluation



22

Object Recognition by
Juseong

Lee

Evaluation


Table 3 has to contain the result that
comparing other recognition
processor



When

hardware

optimization,
Not only
overall

algorithm

but particular algorithm
block

optimization are needed


CORDIC based gradient

and magnitude computation


23

Thanks for Ur listening!





Thanks!


Juseong_lee@korea.ac.kr

24