Sonic Millip3De:
Massively Parallel 3D Stacked Accelerator
for 3D Ultrasound
Richard Sampson* Ming Yang† Siyuan Wei†
Chaitali Chakrabarti† Thomas F. Wenisch*
*University
of Michigan
†Arizona
State University
Portable Medical Imaging Devices
• Medical imaging moving towards portability
– MEDICS (X-Ray CT) [Dasika ‘10]
– Handheld 2D Ultrasound [Fuller ‘09]
• Not just a matter of convenience
– Improved patient health [Gunnarsson ‘00, Weinreb ‘08]
– Access in developing countries
• Why ultrasound?
– Low transmit power [Nelson ‘10]
– No dangers or side-effects
2
Handheld 3D Ultrasound
• 3D has numerous benefits over 2D
– Easier to interpret images
– Greater volumetric accuracy
• … as well as many challenges
– 12k transducers, 10M image points
• 10-20x beyond state of the art
– High raw data bandwidth (6Tb/s)
• Major bottleneck in state of the art
– Tight handheld power budget (5W)
3
Why a Custom Accelerator?
• Software algorithms load/store intensive
– von Neumann designs inefficient
• Large system would require over 700 DSPs
– General purpose CPUs even less efficient
Architecture
Energy/Scanline
(1 fps)
Intel Core i7-2670
25.08J
Single Core
Time/Scanline
4.46s
ARM Cortex-A8
33.04J
132.18s
TI C6678 DSP
2.84J
2.27s
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Contributions
• Iterative delay calculation algorithm
– Reduces storage by over 400x
– Enables streaming data flow
• Sonic Millip3De design
– Leverages 3D die stacking technology
– Transform-select-reduce accelerator framework
• Power and image analysis of Sonic Millip3De
– Negligible change in image quality
– Able to meet 5W power budget by 11nm node
5
Outline
•
•
•
•
Introduction
Ultrasound background
Algorithm design
System design
– Sonic Millip3De
– Select Sub-Unit
• Results and analysis
• Conclusions
6
Ultrasound: Transmit and Receive
Image
Space
𝜏
Receive
Transducer
Focal
Points
Receive Raw
Channel Data
Transmit
Transducer
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
9
Ultrasound: Transmit and Receive
𝜏
10
Ultrasound: Transmit and Receive
𝜏
11
Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
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Ultrasound: Transmit and Receive
𝜏
Each transducer stores array of raw receive data
20
Ultrasound: Image Reconstruction
Image reconstructed from data based on round trip delay
21
Ultrasound: Image Reconstruction
Images from each transducer combined to produce full frame
22
Delay Index Calculation
• Iterate through all image points 𝑃 for each
transducer and calculate delay index 𝜏𝑃
tP =
(
fs
Rp + Rp2 + Xi2 - 2Rp Xi sinq
c
)
• Often done with lookup tables (LUTs) instead
• 50 GB LUT required for target 3D system
23
Challenges of Handheld 3D Ultrasound
• Delay index LUT requires too much storage
– New iterative algorithm reduces necessary
constant storage by 400x
• Peak raw data bandwidth (6Tb/s) infeasible
– Sub-aperture multiplexing reduces peak data rate,
but requires more transmits
• Handheld power budget very tight (5W)
– 3D stacked, highly parallel data streaming design
reconstructs images efficiently
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Iterative Delay Index Calculation
• Deltas between adjacent
focal points on a scanline
form smooth curve
• Fit piecewise quadratic
approx. to delta function
• Two sections sufficient for
negligible error
Section 1
Section 2
25
Sub-aperture Multiplexing
• Peak raw data bandwidth (6Tb/s) infeasible
• Solution: sub-aperture multiplexing
– Transmit multiple times from same location
– Receive with subset of transducers (sub-aperture)
– Sum images together
• Prior work: reduce data rate
• Our design: also reduces HW
and power requirements
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System Design
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System Design
Sonic Millp3De comprises 1,024 parallel pipelines
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System Design: Transducers
Interchangeable CMOS transducer layer; can use older process
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System Design: ADC/Storage
Separate storage layer to reduce wire lengths
30
System Design: Transform-Select-Reduce
Accelerator units in fast, low power process
31
Select Sub-Unit Design
Selects sample closest to each focal point using our algorithm
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Select Sub-Unit Design
Section 1
Section 2
All delays for a scanline estimated using 9 constants
33
Select Sub-Unit Design
Section 1
Section 2
A(n+1)2 + B(n+1) + C = (An2 + Bn + C) + 2An + (A+B)
Adders calculate next iteration of quadratic approximation
34
Select Sub-Unit Design
Section 1
Section 2
Decrementor selects sample for next image focal point
35
Select Sub-Unit Design
Section 1
Section 2
Section decrementor indicates when to change constants
36
Outline
•
•
•
•
Introduction
Ultrasound background
Algorithm design
System design
– Sonic Millip3De
– Select Sub-Unit
• Results and analysis
• Conclusions
37
System Parameters
Parameters
Value
Sub-apertures
12
Transmit Sources
16
Transmits per Frame
192
Transducers per Sub-aperture
1,024
Total Transducers
12,288
Storage per Transducer
4,096 x 12 bits
Focal Points per Scanline
4,096
Image Depth
6 cm
Image Angular Width
π/4
Sampling Frequency
40 MHz
Interpolation Factor
4x
Interpolated Sampling Frequency (fs)
160 MHz
Speed of Sound (tissue)
1,540 m/s
Target Frame Rate
1 fps
38
Image Quality Comparison
Simulations using Field II [Jensen ‘92, ‘95]
Our Design (12 bit)
Ideal
Bits
CNR
Ideal
2.972
14
2.942
13
2.960
12
2.942
11 bit
11
2.536
10
2.233
Our design has negligible difference from ideal system
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Power Analysis and Scaling
20
DRAM
Memory Interface
Network Wires
Accelerator
SRAM
ADC
Transducers
Power (W)
15
10
5
0
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32
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Technology Node
16
11
Can meet 5W by 11nm node
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Conclusions
• 3D die stacked Sonic Millip3De design is able
to meet 5W power budget by 11nm
• Algorithm/HW co-design enables
order-of-magnitude gains
– Power and output quality goals often in conflict
– Need guidance from domain experts to balance
• Architects have much to offer for
application-specific system designs
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Questions?
Special thanks to:
Brian Fowlkes
Oliver Kripfgans
Ron Dreslinski
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