gaussian pyramid: comparative analysis of hardware

© ANAFOCUS

2007

Gaussian Pyramid: Comparative Analysis of

Hardware Architectures

F. D. V. R. Oliveira1, J. G. R. C. Gomes1, J. Fernández-Berni2, R. Carmona-Galán2, R. del

Río2, Á. Rodríguez-Vázquez2

1Universidade Federal do Rio de Janeiro, Brazil

2Instituto de Microeléctronica de Sevilla (IMSE – CNM)

CSIC – Universidad de Sevilla, Spain

Workshop on Architecture

of Smart Cameras

Córdoba

Spain

Embedded Vision Processing Architecture

CONVENTIONAL ARCHITECTURE GPUs DSPs FPGAs …

• Hardware parallelization takes place after having serialized the data previously

• The fact that the imager requires the physical realization of a 2-D array of elementary cells topographically assigned to the corresponding pixel values can be exploited for early parallelization and distributed memory

Embedded Vision Processing Architecture

PROPOSED ARCHITECTURE

• Drastic reduction of memory accesses during low-level processing stages, where pixel-wise operations are common

• Pixel circuitry to accelerate vision algorithms. This circuitry can be implemented in the analog domain for the sake of power and area efficiency

Long-Term Research

Major drawbacks

Reduced fill factor

Large pixel pitch

→ Limited sensitivity

→ Small image size

→ Spatial aliasing

Major achievements

Concept demonstration

Programmable embedded functionalities

Image-to-Decision chain at >1,000fps using 60nW per pixel (industrial chip)

Spatial Gaussian filtering @20nJ/filter

Content-aware HDR acquisition with >145dB intra-frame DR

Major challenges

Implementation of in-pixel embedded functionalities at minimum area cost

Increase hardware-software integration

Drawbacks and Major Challenges

CONVENTIONAL PIXEL

Photo-sensitive

area

pixel pitch 𝑷

Amplification & R

ead

ou

t C

ircu

itry

𝑷𝟐


MULTI-FUNCTIONAL PIXEL

Photo-sensitive

area

pixel pitch 𝑷

Amplification &

Re

ado

ut

Cir

cuit

ry

keep the pixel pitch → reduce the sensitive area

Photo-sensitive

area

pixel pitch 𝑷

Amplification &

Re

ado

ut

Cir

cuit

ry

Processing circuitry

& m

em

ory

keep the sensitive area → reduce the image resolution

Photo-sensitive

area

pixel pitch 𝑷 + ∆

Amplification &

Re

ado

ut

Cir

cuit

ry

Processing circuitry

& m

em

ory

Photo-sensitive

area

Amplification &

Re

ado

ut

Cir

cuit

ry

pixel pitch 𝑷


How to minimally impact on the

image quality while maximally

exploiting the advantages of focal-

plane processing

Fundamental Processing Primitive

[J. Campbell and V. Kazantev, “Using an Embedded Vision Processor to Build an Efficient Object Recognition System,” White Paper, Synopsis, 2015]

CMOS IMPLEMENTATION GAUSSIAN FILTERING

- Basic operation in many vision pipelines



Original full-resolution image

Examples:

Sobel operators

Binomial kernel Original half-resolution image Pre-distorted half-resolution image

Original kernel Reduced kernel

Binomial kernel output


CONVENTIONAL VS. FOCAL-PLANE REALIZATION GAUSSIAN FILTERING

Time Analysis

[F. V. R. Oliveira et al, “Gaussian Pyramid: Comparative Analysis of Hardware Architectures,” IEEE Transactions on Circuits and Systems I, in press, 2017]


Focal-plane processing time

𝑛𝑘: size of the Gaussian kernel

𝑁𝐿𝑒𝑣: number of pyramid levels

𝜏𝐶𝑅: time required to perform one charge redistribution

𝜏𝐴𝐷𝐶: time required to perform the analog-to-digital conversion of one pixel

𝑁𝐴𝐷𝐶: Number of ADCs

𝑀 × 𝑁: Image resolution

Time Analysis



Digital implementation processing time

𝜏𝑀𝑒𝑚: time required to access a single memory position

𝑁𝑏𝑢𝑠𝑀𝑒𝑚: number of parallel accesses to memory

𝜏𝑜𝑝: time required to perform a single MAC operation

Time Analysis



Parameters of time analysis equations

Time Analysis


Energy Analysis

Trickier highly dependent on the architecture and technology parameters; no global parameter either (clock period in time analysis) Standard circuit blocks

MAC unit SRAM memory cell

Energy Analysis

Equations associated with every aspect of the hardware

Focal-plane energy analysis

𝐸𝑝𝑖𝑥𝐶𝑎𝑝𝑡𝑢𝑟𝑒 = 𝐶𝐹𝐷 ∙ 𝑉2𝑑𝑑𝑀

+ 𝐶𝑅𝑠𝑡 ∙ 𝑉2𝑑𝑑𝑀

+ 𝐶𝑇𝑋 ∙ 𝑉2𝑑𝑑𝑀

𝐸𝑐ℎ𝑔𝑅𝑒𝑑𝑖𝑠𝑡𝑟 = (𝑁𝐿𝑒𝑣 − 1)𝑛𝑘 ∙ 2𝑀 ∙ 2𝑁 ∙ (2𝐶𝑛 ∙ 𝑉2𝑑𝑑𝑀

)

.

.

.

Digital implementation energy analysis

𝐸𝑀𝐴𝐶𝑑𝑦𝑛𝑎𝑚𝑖𝑐 = 𝛼 ∙ 𝑁𝑑 ∙ 𝐶𝑛 ∙ 𝑉2𝑑𝑑(𝑁𝑜𝑝 ∙ 3𝜏𝑜𝑝)/𝜏𝐶𝑙𝑘

𝐸𝑀𝐴𝐶𝑠𝑡𝑎𝑡𝑖𝑐 = 𝑁𝑂𝑓𝑓 ∙ 𝑉𝑑𝑑 ∙ 𝐼𝑙𝑒𝑎𝑘 ∙ 𝜏𝐷𝑖𝑔𝑖𝑡𝑎𝑙 . . .


Energy Analysis

Equations associated with every aspect of the hardware

A/D conversion energy analysis

Energy Analysis

Parameters of energy analysis equations


Conclusions

• Hypothesis early vision stages can be accelerated at the focal plane at low energy cost by adding extra per-pixel circuitry

• Comprehensive analysis for Gaussian pyramid generation with minimum pixel area impact

• Major conclusion Potential advantages of focal-plane processing are case-specific

• A/D conversion: critical stage

• Regarding processing time, the focal-plane approach ideally requires one ADC per column to report significant advantages

• Regarding energy saving, the focal-plane approach renders best results for SAR, cyclic or 𝚺𝚫

Questions?

THANK YOU VERY MUCH FOR YOUR ATTENTION

Jorge Fernández-Berni [email protected]

gaussian pyramid: comparative analysis of hardware

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