For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/bdti/embedded-vision-training/videos/pages/choosing-processor-embedded-vision-optio For more information about embedded vision, please visit: http://www.embedded-vision.com Jeff Bier, President of Berkeley Design Technology, Inc. (BDTI) and Founder of the Embedded Vision Alliance, presents the "Choosing a Processor for Embedded Vision: Options and Trends" tutorial at the May 2015 Embedded Vision Summit. Computer vision applications typically demand lots of processor performance. These applications also tend to be complex and fast-changing, so developers need processors that enable quick development of efficient code. And, as vision proliferates into new markets, more and more applications also require low cost and low power consumption. Taken together, this is a tough set of requirements for a processor. Processor suppliers have responded with a diverse range of architectures, combining CPUs with GPUs, DSPs, FPGAs, vision-specific processors and neural network engines. In this presentation, Jeff provides a map to the landscape of processor options for vision applications, highlighting strengths and weaknesses of different processor types. He also illuminates important trends in processors for vision processors (such as specialized neural network engines) and associated development tools (such as support for standard APIs).

1. © 2015 BDTI 1
Jeff Bier – President, BDTI
12 May 2015
Choosing a Processor for Embedded
Vision: Options and Trends

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Processors Enable Vision Everywhere

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The Processor Landscape

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• CPU
• CPU + GPU
• CPU + DSP
• CPU + vision co-processor
• CPU + FPGA
• CPU + DSP + vision co-processor
• Mobile application processor
• …
Numerous, Diverse Processor Chip Options

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Processors come in a surprising variety of forms:
• Silicon IP core (for chip designers)
• Chip
• But not all chips are for sale to all buyers
• System-on-module (SoM)
• Board (development, reference design, production)
• System
Processor Delivery Forms
macrumors.com

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Processing Efficiency vs. Development Effort
Performance/$
Performance/W
Development
Effort

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Trend: Heterogeneous Architectures
Performance/$
Performance/W
Development
Effort

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Recent Trends

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Licensable vision-specific silicon IP co-processor cores:
• Apical Spirit core
• Cadence (Tensilica) IVP core
• CEVA second-generation XM-4 core (announced 2015)
• CogniVue second-generation “Opus” core (announced 2015)
• Synopsys EV52 core (announced 2015)
• videantis v-MP4xxx0HDX vision and compression cores
Trend: Vision-specific Silicon IP
Co-processor Cores

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• Analog Devices BF609
• Freescale S32V
• Inuitive NU3000
• MobileEye EyeQ4
• Movidius Myraid 2
• Texas Instruments TDA3x
Trend: Vision-specific Processor Chips
Inuitive M3 Reference Design
Movidius Myraid 2

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Trend: Convolutional Neural Networks
Degree of Specialization
Software Packages/
Libraries, Frameworks
Architecture Enhancements
Dedicated
Co-processor
None
Architecture Focus

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How much of a processor’s potential performance is ultimate realized,
and how much skill, time and risk is required to develop an application
Depend not only on the processor architecture, but also on the:
• Programming model
• Development tools (availability, quality, performance)
• Optimized software libraries
• APIs (e.g., OpenVX)
• Choice of programming languages (e.g., OpenCL)
Trend: Growing Importance of Tools,
Libraries and APIs

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What You’re Buying
Processor IP,
Cores
Modules
Development
Boards
SoCs
“Processor”

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What You’re Really Buying
Processor IP,
Cores
Modules
Development
Boards
SoCs
Complete Hardware/Software Solutions and
Reference Designs
High-Level CV
Components
Tools,
Lang-
uages,
OSs
Drivers &
Accelerators
Application Software
Training, Support, and Development Services
Ecosystem
“Processor:”
Low-Level CV
Components

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• New Khronos standard API for acceleration of
vision applications
• Promises portability across heterogeneous
processors (CPU, vision co-processor, GPU,
DSP, etc.)
• Developer specifies data flow graph of vision
algorithm kernels
• Kernels may be implemented in any language
• OpenVX framework partitions the graph
among processing engines, handles memory
transfers, etc.
• Designed for mobile, useful in other domains
Trend: OpenVX API for Mobile Vision
Vision
Accelerator
Application
Application
Application
Application
Vision
AcceleratorVision
AcceleratorVision
Accelerator
Khronos Group

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• There are many, diverse processor options for embedded vision
• The landscape is evolving rapidly
• Each vision product is unique
• Selecting a processor is a multi-dimensional optimization problem with
incomplete data
• Allow time to identify and evaluate your options
• A processor is only as good as its tools, libraries, etc.
• Trends to watch:
• Vision-specific processor cores and chips
• CNN-oriented processors
• Tools, libraries and APIs “arms race”
Lessons Learned

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For Product Developers:
• Processor Selection
• Algorithm Design
• Software Optimization
For Technology Suppliers:
• Competitive Analysis
• Sounding Board Service
• Independent Reports
• Demo Development
Meet us at the Embedded Vision Summit Technology Showcase
Or visit www.BDTI.com
BDTI Services

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• www.Embedded-vision.com
• www.BDTI.com
• “Vision: Promise & Challenge of Heterogeneous Processing,”
http://bit.ly/1rjdBT5
• “Processing Options For Implementing Vision Capabilities in Embedded
Systems,” http://bit.ly/1wq16oW
• “Targeting Computer Vision Algorithms to Embedded Hardware,”
http://bit.ly/1nY2wkD
• “Using Heterogeneous Computing for Mobile and Embedded Vision,”
http://bit.ly/1nY2wkD
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