Power-Efficient Medical Image Processing using PUMA

University of MichiganElectrical Engineering and Computer Science

Power-Efficient Medical Image Processing using PUMA

Ganesh Dasika, Kevin Fan1, Scott Mahlke

1Parakinetics, Inc.

University of MichiganAdvanced Computer Architecture Laboratory

University of MichiganElectrical Engineering and Computer Science2

The Advent of the GPGPU• Increasingly popular

substrate for HPC– Astrophysics– Weather Prediction– EDA– Financial instrument pricing– Medical Imaging


Advantages of GPGPUs• High degree of parallelism

– Data-level– Thread-level

• High bandwidth• Commodity products• Increasingly programmable


Disadvantages of GPGPUs• Gap between computation and bandwidth

– 933 GFLOPS : 142 GB/s bandwidth(0.15B of data per FLOP, ~26:1 Compute:Mem Ratio)

• Very high power consumption– Graphics-specific hardware– Multiple thread contexts– Large register files and memories– Fully general datapath

Inefficiencies in allgeneral-purpose architectures


Programmability vs Efficiency?

FPGAs

General PurposeProcessors

DSPsDomain-specific

Accelerators,GPGPUs

Efficiency

Flex

ibilit

y

5

Loop Accelerators,ASICs

???

Highly efficient,some programmability


Medical Image Reconstruction• Compute intensive loops

– 32-bit floating point code– High data/bandwidth requirements

• Increased demand for portability, low power• Much current research focuses on using GPGPUs

for this domain


CT Image reconstruction• X-Ray emitters and

receptors on opposite sides of patients

• Received x-ray intensity corresponds to tissue density

• Multiple scans (“slices”) taken around patient put together to reconstruct 1 2D-image


Projection & Sinogram

Sinogram:All projections

Projection:All ray-sums in a direction

P(t)

f(x,y)

t

y

x

X-raysSinogram

t

p


Example: BackprojectionSinogram Backprojected Image


Example:Filtered Backprojection

Filtered Sinogram Reconstructed Image


Reconstruction: Solve for m’s

m11 m12 m13 m14

m21 m22 m23 m24

m31 m32 m33 m34

m41 m42 m43 m44

16 22 11 10

X-RayEmitter

DetectorValues

Densities

“Human Body“

22

12

10

15


Real Reconstruction Problem• Intensity measured • Rays transmitted

through multiple “pixels”

• Find individual “pixel” values from transmission data

? ? ? ?? ?

? ? ? ?? ?

? ? ? ?? ?

? ? ? ?? ?

? ? ? ?? ?

? ? ? ?? ? 534

417

364

555

501

355

255712

199

512 values

512values

100’s of diagonals @

100’s of angles


Medical Imaging Applications

• Image reconstruction for MRI/CT/PET scans• Large amounts of Vector/Thread-level parallelism• FP-intensive kernels

– Often requiring math library functions• Data-intensive (~5:1 compute:mem ratio)

Benchmark Inner-loop%Scalar/Vector Outer-loop TLP Compute:Mem

ratio

Segmentation Fully vectorizable Do-all 4:1

Laplacian Filtering Fully vectorizable Do-all 3:1Gaussian

ConvolutionFully vectorizable with predicates Do-all 6:1

MRI FH Vector Fully vectorizable Do-all 6:1

MRI Q Vector Fully vectorizable Do-all 5.5:1


• Currently, most scans requiremoving patient to imaging room– Consumes time– Stress on patient

• Studies show benefits of portable, bed-side scanners:– 86% increase in patients suitable for post-stroke thrombolytic

therapy [Weinreb et al, RSNA]– 80-100% drop in scan-related complications

[Gunnarsson et al, J. of Neurosurgery]• New X-Ray emitters push for mAs of current use

Current Concerns: Portability/Power


Current Concerns: Performance• High-accuracy CT algorithms

take too long– Iterative forward/backward

projection– ~Hours on modern CT scanners

instead of minutes• Interventional radiology

– Scans currently takes minutes, but should take seconds

• CT-Flouroscopy– Several scans done in succession


Flexibility• Software algorithms change over time• NRE• Time-to-market

16


PUMA• Tiled architecture• Bandwidth-matched for

improved efficiency• Each tile is a

“Programmable Loop Accelerator” Extern. Interface

CPU Mem Disk …


Programmable Loop Accelerator• Generalize accelerator without losing efficiency

FPGAs

Efficiency, Performance

Flex

ibilit

y

Loop Accelerators,ASICs

ProgrammableLoop Accelerators

18

General PurposeProcessors

DSPsDomain-specific

Accelerators,GPGPUs ???


Designing Loop Accelerators

C Code Loop

19

Hardware

Point-to-point Connections

BR

CRF

+

… …

&

… …

MEM

… …

LocalMem

+

……

*

……

MEM

……

<<

……

LocalMem


Loop Accelerator Architecture


+

… …

&

… …

MEM

… …

LocalMem

FSM

Controlsignals

CRF

BR

Hardware realization of modulo scheduled loopParameterized hardware:• FUs• Shift Register Files

20

• Static Control• Point-to-point Interconnect


Programmable Loop-Accelerator Architecture


+/-

… …

&/|

… …

MEM

… …

LocalMem

ControlMemory

Controlsignals

CRF

BR

RR RRRRRR

Literals

Ring

Functionality Storage Connectivity Control

LA PLACustom FU set Generalized FUs + MOVs

Point-to-point Ring + Port-swapping

Limited size, no addr. Rotating Reg. Files

Hardwired Control Lit. Reg. File + Control Mem

21

+ &

SRF SRFSRFSRF

FSM


MRI.FH PLA• ~0.6 mm2 per tile• 38 FUs• 128 32-bit registers• Inter-FU BW 1 TB/sec

FU Type #

FP-ADDSUB 6

FP-MPY 9

I-ADDSUB 8

MEM 9

I-MPY 1

Other 5


Performance on MRI.FH PLA

MRI.FH MRI.Q CT.segment CT.laplace CT.gauss0.0

0.2

0.4

0.6

0.8

1.0

Non-Generalized Generalized

Norm

alize

d Pe

rform

ance

II preserved

II doubled

Unschedulable


Efficiency on MRI.FH PLA

MRI.FH MRI.Q CT.segment CT.laplace CT.gauss mean0.0

0.2

0.4

0.6

0.8

1.0

Non-Generalized Generalized

Norm

alize

d

Perf/

Powe

r Ef-

ficie

ncy


PUMA System Design• 5 systems designed

around 5 benchmarks• Each composed of

identical tiles• Assume same B/W as

GTX280 (142 GB/s)• # Tiles based on B/W

requirements of benchmark

Extern. Interface

CPU Mem Disk …


System Performance

MRI.FH MRI.Q CT.segment CT.laplace CT.gauss0

20406080

100120140160

Theoretical Realized

GOPs

/sec

4W 3W 2.8W 2.3W 2.7W


Performance vs. GPGPU

PUMA GTS 250 GTX 260 GTX 280 GTX 285 GTX 2950.00.20.40.60.81.01.21.41.61.82.0

Theoretical Realized

TOPs

/sec

63% performance of GTX 295

2X performance of GTS 250


Efficiency vs. GPGPU

MRI.FH MRI.Q CT.segment CT.laplace CT.gauss0

10

20

30

40

50

60

GTS 250 GTX 260 GTX 280 GTX 285 GTX 295

PUM

A Pe

rf/P

ower

ef

-fic

ienc

y ov

er G

PU 22X

54X


Conclusions• Power-efficient accelerator for medical imaging• ASIC-like efficiency with programmability• 63-201% of GPU performance• 22-54X GPU Performance/Power efficiency


Thank you!!

Questions?

Power-Efficient Medical Image Processing using PUMA

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