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Customizing Wide-SIMD Architecturesfor H.264

Sangwon Seo1, Mark Woh1, Scott Mahlke1, Trevor Mudge1

Vijay Sundaram2, Chaitali Chakrabarti2

1 University of Michigan2 Arizona State University

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Customizing Wide-SIMD Architectures for H.264

Outline

Motivation

H.264 Analysis

Proposed Architecture

H.264 Kernel Mappings

Results

Conclusion

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Customizing Wide-SIMD Architectures for H.264

Motivation – Smart Phone

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Reference Images : http://www.apple.com/iphone/gallery/

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Customizing Wide-SIMD Architectures for H.264

Motivation – Inside Smart Phone

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Reference Images : http://idannyb.files.wordpress.com/2008/07/xiuvbfueck3gsdum-large.jpg

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Customizing Wide-SIMD Architectures for H.264

H.264 Design

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Reference Images : I. Richardson, “H.264 and MPEG-4 video compression,” WILEY, 2003

H.264 encoder/decoder reference design

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Customizing Wide-SIMD Architectures for H.264

H.264 – Analysis

H.264 Kernel Algorithms

Heavy SIMD workload

Different natural SIMD widths

High & Medium Thread Level Parallelism

Need to support multiple SIMD widths to maximize the SIMD utilization

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Customizing Wide-SIMD Architectures for H.264

H.264 – Analysis

Example – Deblocking Filter

Two dimensional data are used for multimedia algorithms.

Row or column order memory access works well for one set of edges, but not for the other.

Diagonal memory bank system helps to access blocks along a row or a column.

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Horizontal Filtering

Vertical

Filtering

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Customizing Wide-SIMD Architectures for H.264

H.264 – Analysis

Subgraphs for Innerloops of two kernel algorithms

Large amount of data locality

Large RF power consumption (Read/Write)

Bypass and Temporary buffer support

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Customizing Wide-SIMD Architectures for H.264

H.264 - Analysis

Instruction Pairs

Heavy usage of shuffle and arithmetic operations

Add-Shift : round operation

Sub-Abs : SAD operation

Need to fuse the frequently used instruction pairs

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Customizing Wide-SIMD Architectures for H.264

H.264 - Analysis

Permutation Patterns for Intraprediction

Fixed set of shuffle patterns

Need for programmable shuffle network

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Multiple SIMD widths

Thread-Level Parallelism

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Diagonal Memory Organization

Memory Bank System + Shuffle Network

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Short-lived values stored in temporary buffers

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Short-lived values

Fused Operation

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Customizing Wide-SIMD Architectures for H.264

Modified SIMD architecture

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Shuffle Networks are placed here and there to align data

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Customizing Wide-SIMD Architectures for H.264

Mapping of H.264 Kernels

Intra Prediction

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Customizing Wide-SIMD Architectures for H.264

Results

System Breakdown

H.264 CIF video at 30fps

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Customizing Wide-SIMD Architectures for H.264

Results

Speedup Breakdown

2.13x performance increase on average

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Customizing Wide-SIMD Architectures for H.264

Results

Energy-Delay product comparison

29% energy-delay improvement on average

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Customizing Wide-SIMD Architectures for H.264

Results

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Comparison with latest H.264 encoders

[17] T. C. Chen et.al, “2.8 to 62.7 mW low-power and power-aware H.264 encoder for mobile

applications,” 2007 IEEE Symposium on VLSI Circuits, pp. 222–223, June 2007.

[18] M. Bhatnagar, “TMS320DM6446/3 Power Consumption Summary,” Texas Instruments

Application Reports, http://focus.ti.com/lit/an/spraad6a/spraad6a.pdf, Feb. 2008.

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Customizing Wide-SIMD Architectures for H.264

Conclusion

Key architectural enhancements SIMD partitioning

Diagonal memory bank system

Bypass and temporary buffer support

Fused operation support

Programmable crossbar

Future work Image processing algorithms on SIMD architecture

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Customizing Wide-SIMD Architectures for H.264

Backup Slides

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Customizing Wide-SIMD Architectures for H.264

H.264 – Analysis

Diagonal Memory Organization

Two dimensional data are used for multimedia algorithms.

Blocks along a row or a column need to be accessed easily.

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Customizing Wide-SIMD Architectures for H.264

Mapping of H.264 Kernels

Deblocking Filter

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Customizing Wide-SIMD Architectures for H.264

Mapping of H.264 Kernels

Motion Compensation

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Customizing Wide-SIMD Architectures for H.264

Mapping of H.264 Kernels

Motion Estimation

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