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OpenCL™ and SYCL™ Open Standards
for ADAS Vision Processing
and Machine Learning
Charles Macfarlane, VP Product Marketing
© 2016 Codeplay Software Ltd.2 © 2016 Codeplay Software Ltd. - CONFIDENTIAL2
How do
we get
from
here…
… to
here?
Level 1
•Adaptive
•Assist
Level 2
•Execute
•Automated
manoeuvres
Level 3
•Limited overall
control
Level 4
•Deep self
control
•All conditions
Level 5
•Autonomous
•Stages from
very local to
extensive
journeys
How do we deliver embedded intelligence?
Level 0
•Warnings
These are the SAE levels for
autonomous vehicles. Similar
challenges apply in other
embedded intelligence industries
© 2016 Codeplay Software Ltd.3 © 2016 Codeplay Software Ltd. - CONFIDENTIAL3
We have a mountain to climb
How do
we get to
the top?
When we
don’t know
what the
top looks
like...
… and we
want to get
there in
safe,
manageable,
affordable
steps…
… without
getting lost
on our
own…
… or
climbing the
wrong
mountain
© 2016 Codeplay Software Ltd.4 © 2016 Codeplay Software Ltd. - CONFIDENTIAL4
This presentation will focus on:
• The targets hardware and software platforms that will be able
to deliver the results
• The open standards that will enable solutions to interoperate
• How Codeplay can help deliver embedded intelligence
© 2016 Codeplay Software Ltd.5 © 2016 Codeplay Software Ltd. - CONFIDENTIAL5
Where do we need to go?
“On a 100 millimetre-squared chip, Google needs something
like 50 teraflops of performance”
- Daniel Rosenband (Google’s self-driving car
project) at HotChips 2016
© 2016 Codeplay Software Ltd.6 © 2016 Codeplay Software Ltd. - CONFIDENTIAL6
1
1
2
4
8
16
32
64
128
256
512
1,024
2,048
4,096
8,192
16,384
32,768
65,536
Google target
Desktop GPU
Integrated GPU
Smartphone GPU
Smartphone CPU
Desktop CPU
Performance trends
GFLOPS
Year of introduction
These trend
lines seem to
violate the rules
of physics…
© 2016 Codeplay Software Ltd.7 © 2016 Codeplay Software Ltd. - CONFIDENTIAL7
How do we get there from here?
We need to write
software today
for platforms that
cannot be built yet
We need to start with
simpler systems
that are not fully
autonomous
We need to validate
the systems as safe
© 2016 Codeplay Software Ltd.8 © 2016 Codeplay Software Ltd. - CONFIDENTIAL8
Two models of software development
Design a
model
Validate
model
Select
platform
Implement
on
platform
Write software
Select platform
Optimize for
platform
Validate whole
platform
Design next
version
Which method can get us all the way to full autonomy?
© 2016 Codeplay Software Ltd.9 © 2016 Codeplay Software Ltd. - CONFIDENTIAL9
Desirable Solution Development
Well Defined Middleware
Software Application
Hardware & Low-Level Software
Write
software
Evaluate
Architect
Select
Platform
Develop
Platform
Write software
Optimize for
platform
Validate whole
platform
Evaluate
Software
© 2016 Codeplay Software Ltd.10 © 2016 Codeplay Software Ltd. - CONFIDENTIAL10
The different levels of programming model
Device-specific
programming
•Assembly
language
•VHDL
•Device-specific C-
like programming
models
Higher-level
language
enabler
•NVIDIA PTX
•HSA
•OpenCL SPIR
•SPIR-V
C-level
programming
•OpenCL C
•DSP C
•MCAPI/MTAPI
C++-level
programming
•SYCL
•CUDA
•HCC
•C++ AMP
Graph
programming
•OpenCV
•OpenVX
•Halide
•VisionCpp
•TensorFlow
•Caffe
© 2016 Codeplay Software Ltd.11 © 2016 Codeplay Software Ltd. - CONFIDENTIAL11
Why graph programming?
When you scale the
number of cores:
• You don’t scale the
number of memory ports
• Your compute
performance increases
• But your off-chip memory
bandwidth does not
Therefore:
• You need to
reduce off-chip
memory
bandwidth by
processing
everything on-
chip
• This is achieved by
tiling
However,
writing tiled
image pipelines
is hard
If we build up a graph of operations (e.g. convolutions) and then have a
runtime system split into fused tiled operations across an entire system-on-
chip, we get great performance
© 2016 Codeplay Software Ltd.12 © 2016 Codeplay Software Ltd. - CONFIDENTIAL12
The route to full autonomy
• Graph programming
• This is the most widely-adopted approach to machine vision and machine
learning
• Open standards
• This lets you develop today for future architectures
© 2016 Codeplay Software Ltd.13 © 2016 Codeplay Software Ltd. - CONFIDENTIAL13
Which model should we choose?
Device-specific
programming
•Assembly
language
•VHDL
•Device-specific C-
like programming
models
Higher-level
language
enabler
•NVIDIA PTX
•HSA
•OpenCL SPIR
•SPIR-V
C-level
programming
•OpenCL C
•DSP C
•MCAPI/MTAPI
C++-level
programming
•SYCL
•CUDA
•HCC
•C++ AMP
Graph
programming
•OpenCV
•OpenVX
•Halide
•VisionCpp
•TensorFlow
•Caffe
© 2016 Codeplay Software Ltd.14 © 2016 Codeplay Software Ltd. - CONFIDENTIAL14
They are not alternatives, they are layers
Device-specific programming
Assembly language VHDL Device-specific C-like programming models
Higher-level language enabler
NVIDIA PTX HSA OpenCL SPIR SPIR-V
C/C++-level programming
SYCL CUDA HCC C++ AMP OpenCL
Graph programming
OpenCV OpenVX Halide VisionCpp TensorFlow Caffe
© 2016 Codeplay Software Ltd.15 © 2016 Codeplay Software Ltd. - CONFIDENTIAL15
Can specify, test and validate each layer
Device-specific programming
Device-specific specification Device-specific testing and validation
Higher-level language enabler
SPIR/SPIR-V/HSAIL specs Conformance testsuites
C/C++-level programming
OpenCL/SYCL specs Clsmith testsuite Conformance testsuites Wide range of other testsuites
Graph programming
Validate graph models Validate the code using standard tools
© 2016 Codeplay Software Ltd.16 © 2016 Codeplay Software Ltd. - CONFIDENTIAL16
For Codeplay, these are our layer choices
Device-
specific
programming
• LLVM
Higher-level
language
enabler
• OpenCL SPIR
C/C++-level
programming
• SYCL
Graph
programming
• TensorFlow
• OpenCV
We have chosen a layer of standards, based on
current market adoption
• TensorFlow and OpenCV
• SYCL
• OpenCL (with SPIR)
• LLVM as the standard compiler back-end
The actual choice of standards may change based on
market dynamics, but by choosing widely adopted
standards and a layering approach, it is easy to adapt
© 2016 Codeplay Software Ltd.17 © 2016 Codeplay Software Ltd. - CONFIDENTIAL17
For Codeplay, these are our products
Device-
specific
programming
• LLVM
Higher-level
language
enabler
• OpenCL SPIR
C/C++-level
programming
• SYCL
Graph
programming
• TensorFlow
• OpenCV
© 2016 Codeplay Software Ltd.18 © 2016 Codeplay Software Ltd. - CONFIDENTIAL18
Where Codeplay fits in
Semiconductor
companies
Software tools and
platforms: Codeplay
Machine intelligence
software
© 2016 Codeplay Software Ltd.19 © 2016 Codeplay Software Ltd. - CONFIDENTIAL19
Company
Based in
Edinburgh,
Scotland
~60 staff, mostly engineering
License and customize technologies for
semiconductor companies
ComputeAorta and ComputeCpp:
implementations of OpenCL, Vulkan and SYCL
15+ years of experience in heterogeneous
solutions
© 2016 Codeplay Software Ltd.20 © 2016 Codeplay Software Ltd. - CONFIDENTIAL20
Further information
• OpenCL https://www.khronos.org/opencl/
• OpenVX https://www.khronos.org/openvx/
• HSA http://www.hsafoundation.com/
• SYCL http://sycl.tech
• OpenCV http://opencv.org/
• Halide http://halide-lang.org/
• VisionCpp https://github.com/codeplaysoftware/visioncpp
/codeplaysoft@codeplaysoft codeplay.com
Charles.Macfarlane@Codeplay.com
VP Product Marketing

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Codeplay Software - Open Standards for Automotive Vision Processing & Machine Learning

  • 1. OpenCL™ and SYCL™ Open Standards for ADAS Vision Processing and Machine Learning Charles Macfarlane, VP Product Marketing
  • 2. © 2016 Codeplay Software Ltd.2 © 2016 Codeplay Software Ltd. - CONFIDENTIAL2 How do we get from here… … to here? Level 1 •Adaptive •Assist Level 2 •Execute •Automated manoeuvres Level 3 •Limited overall control Level 4 •Deep self control •All conditions Level 5 •Autonomous •Stages from very local to extensive journeys How do we deliver embedded intelligence? Level 0 •Warnings These are the SAE levels for autonomous vehicles. Similar challenges apply in other embedded intelligence industries
  • 3. © 2016 Codeplay Software Ltd.3 © 2016 Codeplay Software Ltd. - CONFIDENTIAL3 We have a mountain to climb How do we get to the top? When we don’t know what the top looks like... … and we want to get there in safe, manageable, affordable steps… … without getting lost on our own… … or climbing the wrong mountain
  • 4. © 2016 Codeplay Software Ltd.4 © 2016 Codeplay Software Ltd. - CONFIDENTIAL4 This presentation will focus on: • The targets hardware and software platforms that will be able to deliver the results • The open standards that will enable solutions to interoperate • How Codeplay can help deliver embedded intelligence
  • 5. © 2016 Codeplay Software Ltd.5 © 2016 Codeplay Software Ltd. - CONFIDENTIAL5 Where do we need to go? “On a 100 millimetre-squared chip, Google needs something like 50 teraflops of performance” - Daniel Rosenband (Google’s self-driving car project) at HotChips 2016
  • 6. © 2016 Codeplay Software Ltd.6 © 2016 Codeplay Software Ltd. - CONFIDENTIAL6 1 1 2 4 8 16 32 64 128 256 512 1,024 2,048 4,096 8,192 16,384 32,768 65,536 Google target Desktop GPU Integrated GPU Smartphone GPU Smartphone CPU Desktop CPU Performance trends GFLOPS Year of introduction These trend lines seem to violate the rules of physics…
  • 7. © 2016 Codeplay Software Ltd.7 © 2016 Codeplay Software Ltd. - CONFIDENTIAL7 How do we get there from here? We need to write software today for platforms that cannot be built yet We need to start with simpler systems that are not fully autonomous We need to validate the systems as safe
  • 8. © 2016 Codeplay Software Ltd.8 © 2016 Codeplay Software Ltd. - CONFIDENTIAL8 Two models of software development Design a model Validate model Select platform Implement on platform Write software Select platform Optimize for platform Validate whole platform Design next version Which method can get us all the way to full autonomy?
  • 9. © 2016 Codeplay Software Ltd.9 © 2016 Codeplay Software Ltd. - CONFIDENTIAL9 Desirable Solution Development Well Defined Middleware Software Application Hardware & Low-Level Software Write software Evaluate Architect Select Platform Develop Platform Write software Optimize for platform Validate whole platform Evaluate Software
  • 10. © 2016 Codeplay Software Ltd.10 © 2016 Codeplay Software Ltd. - CONFIDENTIAL10 The different levels of programming model Device-specific programming •Assembly language •VHDL •Device-specific C- like programming models Higher-level language enabler •NVIDIA PTX •HSA •OpenCL SPIR •SPIR-V C-level programming •OpenCL C •DSP C •MCAPI/MTAPI C++-level programming •SYCL •CUDA •HCC •C++ AMP Graph programming •OpenCV •OpenVX •Halide •VisionCpp •TensorFlow •Caffe
  • 11. © 2016 Codeplay Software Ltd.11 © 2016 Codeplay Software Ltd. - CONFIDENTIAL11 Why graph programming? When you scale the number of cores: • You don’t scale the number of memory ports • Your compute performance increases • But your off-chip memory bandwidth does not Therefore: • You need to reduce off-chip memory bandwidth by processing everything on- chip • This is achieved by tiling However, writing tiled image pipelines is hard If we build up a graph of operations (e.g. convolutions) and then have a runtime system split into fused tiled operations across an entire system-on- chip, we get great performance
  • 12. © 2016 Codeplay Software Ltd.12 © 2016 Codeplay Software Ltd. - CONFIDENTIAL12 The route to full autonomy • Graph programming • This is the most widely-adopted approach to machine vision and machine learning • Open standards • This lets you develop today for future architectures
  • 13. © 2016 Codeplay Software Ltd.13 © 2016 Codeplay Software Ltd. - CONFIDENTIAL13 Which model should we choose? Device-specific programming •Assembly language •VHDL •Device-specific C- like programming models Higher-level language enabler •NVIDIA PTX •HSA •OpenCL SPIR •SPIR-V C-level programming •OpenCL C •DSP C •MCAPI/MTAPI C++-level programming •SYCL •CUDA •HCC •C++ AMP Graph programming •OpenCV •OpenVX •Halide •VisionCpp •TensorFlow •Caffe
  • 14. © 2016 Codeplay Software Ltd.14 © 2016 Codeplay Software Ltd. - CONFIDENTIAL14 They are not alternatives, they are layers Device-specific programming Assembly language VHDL Device-specific C-like programming models Higher-level language enabler NVIDIA PTX HSA OpenCL SPIR SPIR-V C/C++-level programming SYCL CUDA HCC C++ AMP OpenCL Graph programming OpenCV OpenVX Halide VisionCpp TensorFlow Caffe
  • 15. © 2016 Codeplay Software Ltd.15 © 2016 Codeplay Software Ltd. - CONFIDENTIAL15 Can specify, test and validate each layer Device-specific programming Device-specific specification Device-specific testing and validation Higher-level language enabler SPIR/SPIR-V/HSAIL specs Conformance testsuites C/C++-level programming OpenCL/SYCL specs Clsmith testsuite Conformance testsuites Wide range of other testsuites Graph programming Validate graph models Validate the code using standard tools
  • 16. © 2016 Codeplay Software Ltd.16 © 2016 Codeplay Software Ltd. - CONFIDENTIAL16 For Codeplay, these are our layer choices Device- specific programming • LLVM Higher-level language enabler • OpenCL SPIR C/C++-level programming • SYCL Graph programming • TensorFlow • OpenCV We have chosen a layer of standards, based on current market adoption • TensorFlow and OpenCV • SYCL • OpenCL (with SPIR) • LLVM as the standard compiler back-end The actual choice of standards may change based on market dynamics, but by choosing widely adopted standards and a layering approach, it is easy to adapt
  • 17. © 2016 Codeplay Software Ltd.17 © 2016 Codeplay Software Ltd. - CONFIDENTIAL17 For Codeplay, these are our products Device- specific programming • LLVM Higher-level language enabler • OpenCL SPIR C/C++-level programming • SYCL Graph programming • TensorFlow • OpenCV
  • 18. © 2016 Codeplay Software Ltd.18 © 2016 Codeplay Software Ltd. - CONFIDENTIAL18 Where Codeplay fits in Semiconductor companies Software tools and platforms: Codeplay Machine intelligence software
  • 19. © 2016 Codeplay Software Ltd.19 © 2016 Codeplay Software Ltd. - CONFIDENTIAL19 Company Based in Edinburgh, Scotland ~60 staff, mostly engineering License and customize technologies for semiconductor companies ComputeAorta and ComputeCpp: implementations of OpenCL, Vulkan and SYCL 15+ years of experience in heterogeneous solutions
  • 20. © 2016 Codeplay Software Ltd.20 © 2016 Codeplay Software Ltd. - CONFIDENTIAL20 Further information • OpenCL https://www.khronos.org/opencl/ • OpenVX https://www.khronos.org/openvx/ • HSA http://www.hsafoundation.com/ • SYCL http://sycl.tech • OpenCV http://opencv.org/ • Halide http://halide-lang.org/ • VisionCpp https://github.com/codeplaysoftware/visioncpp