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Confidential and Proprietary
Nauto is launching a connected camera for professional drivers
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- Drive more than most
consumers
- Exposed to passenger
and driver liability
- Driver quality unknown -
GPU-B ASED D EEP L EARNING IN C LOUD AND E MBEDDED S YSTEMS F - - PowerPoint PPT Presentation
GPU-B ASED D EEP L EARNING IN C LOUD AND E MBEDDED S YSTEMS F - - PowerPoint PPT Presentation
GPU-B ASED D EEP L EARNING IN C LOUD AND E MBEDDED S YSTEMS F REDERICK S OO , CTO April 4, 2016 Confidential and Proprietary Nauto is launching a connected camera for professional drivers Drive more than most consumers Exposed to
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Massive shift in transportation due to synergistic technologies
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Autonomous
90% reduction in accidents
Connected Electric Shared
$0.08 / mile
85% efficient drivetrain 50-70% utilization Fleet
- ptimization
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Why use deep learning?
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Good at visual tasks Scalable Deployable
Most important for NAUTO
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Small brains have a lot of functionality
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26 billion neurons 1 million 10 million 100 million 20 watts 1mW 10mW 100mW
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Required performance depends on use case
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Small changes in F1 with size
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- Large networks can be
used in later stages of cascade
- Order of magnitude
improvements in speed with basic exploration
- Always worth
measuring performance/size tradeoff
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Test your chipsets - algorithm speed important but not entire story
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30 60 90 120 150 A B C D E Nauto CNN forward pass (msec) Embedded SoC
- Chipsets released in
2014, 2015 and 2016
- Pricing varying from
$25 to $60+
- Varying degrees of
HW/SW support
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Algorithm is not the bottleneck
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Image processing Conversion to CNN space CNN forward pass Other steps 30msec 30msec … msec 15msec
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Entire system must be optimized
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Collect data Label Train Deploy years months months months/years Pre-GPU
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Entire system must be optimized
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Collect data Label Train Deploy weeks months months months/years Post-GPU years months months months/years Pre-GPU
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Entire system must be optimized
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Collect data Label Train Deploy weeks months months months/years Post-GPU days weeks weeks weeks Nauto prototype years months months months/years Pre-GPU
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Entire system must be optimized
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Collect data Label Train Deploy weeks months months months/years Post-GPU days weeks weeks weeks Nauto prototype years months months months/years Pre-GPU Nauto at- scale ? ? ? ?
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Easy to think of optimization; hard to think of system
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Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.
Donald Knuth
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Lessons
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- Embedded pipeline as important as raw CNN
performance
- Match algorithm performance to use case
- Overall system performance (data acquisition,
labeling, training) is where big progress to be made
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The future is in distributed awareness
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Real world search
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Team
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