9/21/2011
Implementing
MATLAB and Simulink Algorithms
g
on FPGAs
Stefano Olivieri
Senior Application Engineer
MathWorks
Marco Visintini
Sales Account Manager
MathWorks
Daniele Bagni
DSP Specialist EMEA
Xilinx
© 2011 The MathWorks, Inc.1
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
2
1
9/21/2011
Introducing The Speakers
Xilinx:
 Daniele Bagni
DSP Specialist EMEA
MathWorks:
 Stefano Olvieri
Senior Application Engineer
Signal Processing and Communication

Marco Visintini
Sales Account Manager
3
MathWorks and Xilinx Goals

MathWorks: accelerate the pace of engineering and
science by providing best in class Software for:
– Development
De elopment and verification
erification of algorithms and control logic
– Embedded Systems implementation

Xilinx: providing best in class Silicon including FPGAs
and embedded system hardware platforms :
– Offers FPGAs and Zynq – an Extensible Processing Platform
– Partner
P t
with
ith MathWorks
M thW k to
t provide
id an integrated
i t
t d workflow
kfl

Purpose of the joint seminar:
– to demonstrate a Model-Based Design workflow for FPGAs from first idea down to the Hardware.
4
2
9/21/2011
MathWorks at a Glance

Headquarters:
N ti k M
Natick,
Massachusetts
h
tt US

Other US Locations:
California, Michigan,
Texas, Washington DC

Europe:
France, Germany, Italy,
Spain, the Netherlands,
Sweden, Switzerland, UK

Asia-Pacific:
Australia, China, India,
Japan, Korea

Worldwide training
and consulting

Distributors in 25 countries
Earth’s topography on an equidistant cylindrical projection,
created with MATLAB and Mapping Toolbox.
5
MathWorks Today





1984
Revenues ~$600M in 2010
Privately held
More than 2000 employees worldwide
Worldwide revenue balance:
45% North America, 55% international
More than 1 million users in 175+ countries
1989
1994
1999
2004
2009
6
3
9/21/2011
Key Industries









Aerospace and Defense
Automotive
Biotech and Pharmaceutical
Communications
Education
Electronics and Semiconductors
Energy Production
Financial Services
Industrial Automation and
Machinery
7
Who is Who???

Who is a System Engineer?

Wh iis an FPGA designers
Who
d i
?

Who is using MATLAB?

Who is using Simulink?
10
4
9/21/2011
Your Expectations Beyond the Agenda...
11
Corner Detection in Video Mosaicking
(A Brief Example)
13
5
9/21/2011
Things to remember ….
DESIGN
Algorithm
Development
MATLAB
Simulink
Stateflow

Use Model-Based Design to provide
an integrated workflow

Speed up algorithm development
with a unified design environment

Automate manual steps in
FPGA implementation to enable
shorter iteration cycles
y

Integrate FPGA development tools to
reduce verification time
14
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
15
6
9/21/2011
Why do we use FPGAs?




Customized interfaces to
peripherals
Memory
Memory
Memory
High-speed communication
interfaces to other
processors
Finite state machines, digital
logic, timing and memory
control
We are going
to focus on
this use case
today
Bridge
Analog I/O
FPGA
ARM
Digital I/O
High speed, highly parallel
DSP Algorithms or Control
Algorithms
DSP
DSP
Algorithms
16
Separate Views of DSP Implementation
System Designer
FPGA Designer
Algorithm Design
System Test Bench
RTL Design
Verification
Fixed-Point
Environment Models
IP Interfaces
Behavioral Simulation
HW Architecture
Timing / Control Logic
Analog Models
Architecture Exploration
Digital Models
Algorithms / IP
Algorithms / IP
Timing Simulation
Implement Design
FPGA Requirements
Hardware Specification
Test Stimulus
Functional Simulation
Static Timing Analysis
Back Annotation
Synthesis
Map
Place & Route
FPGA Hardware
17
7
9/21/2011
Where do you spend most of your time?
System Designer


Algorithm Design
System Test Bench
Fixed-Point
Environment Models
Timing / Control Logic
Analog Models
Architecture Exploration
Digital Models
Algorithms / IP
Algorithms / IP



FPGA Requirements

Simulating designs?
Creating designs and test
benches?
Analyzing and combining results
from multiple tools?
Exploring implementation ideas
and architectures?
Floating point to fixed-point?
Writing HW specifications?
Hardware Specification
Test Stimulus


Iterating over designs with the
FPGA designer?
Blaming the FPGA designer?
18
Where do you spend most of your time?





Simulating designs and validating
against HW specs?
Creating designs and writing test
benches?
Hardware architecture design?
Writing interfaces to existing IP?
Synthesis, Map, PAR cycles?
FPGA Designer
RTL Design
Verification
IP Interfaces
Behavioral Simulation
Hardware Architecture
Timing Simulation
Implement Design


Iterating over designs with the
system designer?
Blaming the system designer?
Functional Simulation
Static Timing Analysis
Back Annotation
Synthesis
Map
ap
Place & Route
FPGA Hardware
19
8
9/21/2011
A Few Ways to Reduce Development Time
1.
2.
3.
4.
Increase simulation speed
Simplify design entry, system test harness
creation, and exploration
Shorter iteration cycles required for RTL design
& verification
Integrate the separate workflows to facilitate
collaboration, re-use, and prototyping
20
Model-Based Design for Implementation
MATLAB® and Simulink®
Algorithm and System Design
Algorithm Design
System Test Bench
RTL Design
Verification
Fixed-Point
Environment Models
IP Interfaces
Behavioral Simulation
Hardware Architecture
Timing / Control Logic
Analog Models
Architecture Exploration
Digital Models
Algorithms / IP
Algorithms / IP
Timing Simulation
Implement Design
FPGA Requirements
Hardware Specification
Test Stimulus
Functional Simulation
Static Timing Analysis
Back Annotation
Synthesis
Map
Place & Route
FPGA Hardware
21
9
9/21/2011
Model-Based Design for Implementation
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
RTL Design
Verification
IP Interfaces
Behavioral Simulation
Hardware Architecture
Automatic HDL
Code Generation
Functional Simulation
Static Timing Analysis
Timing Simulation
Implement Design
Back Annotation
Synthesis
Map
Place & Route
FPGA Hardware
22
Model-Based Design for Implementation
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Verification
Behavioral Simulation
Automatic HDL
Code Generation
Functional Simulation
HDL CoCo-Simulation
Static Timing Analysis
Behavioral Simulation
Timing Simulation
Implement Design
Back Annotation
Synthesis
Map
Place & Route
FPGA Hardware
23
10
9/21/2011
Model-Based Design for Implementation
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
Verification
Functional Simulation
HDL CoCo-Simulation
Static Timing Analysis
Behavioral Simulation
Timing Simulation
Implement Design
Back Annotation
Back Annotation
Synthesis
Map
Implement Design
Verification
Place & Route
FPGA Hardware
Functional Simulation
Synthesis
Static Timing Analysis
Map
Timing Simulation
Place & Route
24
Model-Based Design for Implementation
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
HDL CoCo-Simulation
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
FPGA Hardware
Functional Simulation
Static Timing Analysis
Timing Simulation
FPGA Hardware
FPGA-inFPGAin-the
the--Loop
25
11
9/21/2011
Why Model-Based Design?
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
HDL CoCo-Simulation

Use Model-Based Design to
provide an integrated
workflow

Speed up algorithm
development with a unified
design environment

Automate manual steps in
FPGA implementation to
enable shorter iteration
cycles

Integrate FPGA development
tools to reduce verification
time
27
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
Functional Simulation
Static Timing Analysis
Timing Simulation
FPGA Hardware
FPGA-inFPGAin-the
the--Loop
AT4 wireless Increases Internal Test
Coverage to Over 90% for LTE Physical
Layer Test Equipment Designs
Ch
Challenge
ll
Develop test systems for LTE wireless equipment
AT4 wireless LTE layer 1 tester.
Solution
Use MATLAB and Simulink to design and simulate the
LTE physical layer, verify the FPGA implementation,
and analyze test results
Results
 Internal test coverage increased to over 90%
 Test harness reused throughout the project
life cycle
 Development effort reduced by 25–30%
“MATLAB is a universal language that
makes it easy to exchange algorithms
and test results across our team. Our
physical layer model in MATLAB and
Simulink enabled us to better
understand the LTE specifications, and
Model-Based Design enabled us to
verify that our FPGA implementation
conformed to those specifications.”
Francisco Javier Campos
AT4 wireless
Link to user story
29
12
9/21/2011
Semtech Speeds Development of
Digital Receiver FPGAs and ASICs
Challenge
g
Accelerate the development of optimized digital
receiver chains for wireless RF devices
The Semtech SX1231 wireless transceiver.
Solution
Use MathWorks tools for Model-Based Design to
generate production VHDL code for rapid FPGA
and ASIC implementation
Results
 Prototypes created 50% faster
 Verification time reduced from weeks to days
 Optimized, better-performing design delivered
“Writing VHDL is tedious, and the
handwritten code still needs to be
verified. With Simulink and Simulink
HDL Coder, once we have simulated
the model we can generate VHDL
directly and prototype an FPGA. It
saves a lot of time, and the generated
code contains some optimizations we
hadn’t thought of.”
Frantz Prianon
Semtech
Link to user story
30
Case Study: Corner Detection Algorithm
© 2011 The MathWorks, Inc.
31
13
9/21/2011
Harris-Stephens’ Corner Detection

Corner detection is used in many Image Processing
applications
– Image mosaicking
– Tracking
– Object recognition
32
Harris-Stephens’ Corner Detection
Horizontal Gradient
Corner Metric Mc
Vertical Gradient
Sobel Edge
Filter
Calculate Corner
Metrics
Threshold &
Find Local
Maxima
33
14
9/21/2011
From Algorithm to Synthesizable RTL
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
HDL CoCo-Simulation
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
Functional Simulation
Static Timing Analysis
Timing Simulation
FPGA Hardware
FPGA-inFPGAin-the
the--Loop
34
Flexible Design Environment
Design, Simulation and Implementation


Choice of best modeling methods
(Simulink, MATLAB and Stateflow)
Integrate with MATLAB Algorithm Design
35
15
9/21/2011
Fixed Point Analysis
Corner Detection

Convert floating point to optimized fixed point models
– Automatic tracking of signal range (also intermediate quantities)
– Word / Fraction lengths recommendation
Automatically

Bit-true models in the same environment
identify and solve
fixed point issues
36
Automatic HDL Code Generation
Corner Detection
Automatically generate bit true,
cycle accurate HDL code from
Simulink, MATLAB and Stateflow
Full bi-directional
traceability!!
Requirements
37
16
9/21/2011
Simulink Library Support for HDL
HDL Supported Blocks

170 blocks supported

Core Simulink Blocks
–

Signal Processing Blocks
–

Basic and Array Arithmetic, Look-Up Tables,
Signal Routing (Mux/Demux, Delays,
Selectors), Logic & Bit Operations, Dual and
single port RAMs, FIFOs, CORDICs
NCOs,, FFTs,, Digital
g
Filters (FIR,
(
, IIR,, Multirate, Adaptive), Rate Changes (Up &Down
Sample), Statistics (Min/Max)
Communications Blocks
–
Psuedo-random Sequence Generators,
Modulators / Demodulators, Interleavers /
Deinterleavers, Viterbi Decoders
38
MATLAB & Stateflow for HDL
HDL Supported Blocks

MATLAB
– Relevant subset of the MATLAB
language for modeling and generating
HDL implementations
– eml_hdl_design_patterns:
Useful MATLAB Function Block Design
Patterns for HDL

Stateflow
– Graphical tool for modeling Mealy
and Moore Finite State Machines
39
17
9/21/2011
Integrating Legacy HDL Code
HDL Supported Blocks
Integrate legacy HDL code
in Simulink using black
boxes
Configure the interface to
legacy HDL code
EDA Simulator Link is a
special black box
40
Summary: Modeling and Code Generation

Model-Based Design provides an integrated workflow
– Optimized design on a System Level

Speed up algorithm development with a unified design
environment
– Collaborate with other engineers
– Use Simulink blocks, Stateflow or MATLAB for modeling and
implementation

Shorter iteration cycles
– Assisted Fixed-Point Conversion
– Automatic HDL Code Generation
41
18
9/21/2011
Break
42
Things to remember ….
DESIGN
Algorithm
Development
MATLAB
Simulink
Stateflow

Use Model-Based Design to provide
an integrated workflow

Speed up algorithm development
with a unified design environment

Automate manual steps in
FPGA implementation to enable
shorter iteration cycles
y

Integrate FPGA development tools to
reduce verification time
43
19
9/21/2011
Wh t would
What
ld you like
lik to
t gett
from automatic code
generation?
44
Hardware Design Challenges:
Optimizing for Speed, Area or Power
DESIGN
Algorithm
Development
MATLAB
Simulink
Stateflow

Optimize HDL code

Verify optimized HDL

Place & Route

Analyze result
45
20
9/21/2011
IIR Low Pass Filter
Direct-Form II Transposed SOS
46
From Algorithm to Optimized RTL
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
HDL CoCo-Simulation
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
Functional Simulation
Static Timing Analysis
Timing Simulation
FPGA Hardware
FPGA-inFPGAin-the
the--Loop
47
21
9/21/2011
Hardware Design Challenges:
Speed Optimization
Finding the critical path in your model
can be challenging
48
Demo: HDL Workflow Advisor
>> Choose target workflow:
•
•
•
FPGA-in-the-Loop
FPGA Turnkey
Design exploration:
Generic ASIC/FPGA
Choose FPGA target
49
22
9/21/2011
Demo: HDL Workflow Advisor
Perform relevant
checks for HDL
code generation
50
Demo: HDL Workflow Advisor
Set options and generate
automatically HDL code
51
23
9/21/2011
Demo: HDL Workflow Advisor
Create FPGA project
Run P&R
-andAnnotate timing information
Automated workflow 
from model to FPGA Analysis &
Implementation
52
Identifying the critical path
Speed Optimization
Critical Path highlighting:
 Visual representation of critical path in your model
 Easier to identify bottlenecks of your model
53
24
9/21/2011
Balancing pipeline registers
Speed Optimization
parallel
paths


critical path
Multiple parallel paths through your model
High risk to have unmatched latencies
54
Demo: Configuring Pipelining Options
Speed Optimization
55
25
9/21/2011
Distributed Pipelining
Speed Optimization



Distributed pipelining (model retiming)
Automatic balancing of pipeline registers
(focus on critical path only)
You are in full control of your pipelining strategy
 Bottom-up and top-down
56
Distributed Pipelining
Speed Optimization
Minimum period: 23.796ns
Maximum Frequency: 42.024MHz
42 024MHz
Section 1
Section 3
Section 2
Device,package,speed:
xc5vsx50t,ff1136,-1
57
26
9/21/2011
Distributed Pipelining
Speed Optimization
Minimum period: 9.379ns
Maximum Frequency: 106.62MHz
106 62MHz
Section 3
Section 2
Device,package,speed:
xc5vsx50t,ff1136,-1
58
Hardware Design Challenges:
Area Optimization
X
X
X
X
MUX
DEMUX
SCHEDULING
X
X
X
59
27
9/21/2011
IIR Low Pass Filter
Direct-Form II Transposed SOS
Challenges:
 Data dependent resources to be shared
 Feedback loops
 Vectorized inputs
60
Demo: Configuring Sharing Options
Area Optimization
61
28
9/21/2011
Resource Sharing and Streaming
Area Optimization



Easy to explore different sharing options
Direct feedback through resource utilization report
Prove correctness through validation models
62
Hardware Design Challenges:
Power Optimization

Power Dissipation = Static Power + Dynamic Power
– Static Power = Due to transistor leakage current

Significant in smaller silicon geometries
– Dynamic Power = CV2f

Function of load capacitance, operating frequency, and voltage swing
63
29
9/21/2011
Better Algorithm Design
Power Optimization

Steps To Reduce Power
–
–
–
–
Smaller/Efficient Designs
Reduce Clock Frequency
Control Subsystem Execution (enabled/triggered subsystems)
Low Power Design Libraries/FPGA Devices
64
Multi-rate Models to Reduce Clock Frequency
Power Optimization


Cycle accurate simulation and implementation
Multiple
p or single
g clock implementation
p
clk_enable
clk
clk_enable
enb_1_2_1
Timing Controller
enb_1_2_0
65
30
9/21/2011
Control Subsystem Execution
Power Optimization
Enabled Subsystems

Modules can be enabled and disabled
66
Control Subsystem Execution
Power Optimization
Triggered Subsystems

Modules can be triggered:
gg
rising
g / falling
g / either edge
g
67
31
9/21/2011
Harris-Stephens’ Corner Detection

How do these techniques work with our Corner
Detection algorithm??
68
Summary: Corner Detection Demo
Multipliers
Adders/Subtractors
Registers


154
90
852
Multipliers
Adders/Subtractors
Registers
RAMs
0
RAMs
Multiplexers
2
Multiplexers
6
46
679
4
302
Easy approach to explore different implementations
No costly mistakes
69
32
9/21/2011
Summary: Code Generation Optimizations

Shorter iteration cycles
– Automatic HDL code generation

Flexible automatic HDL Code generation
–
–
–
–
–
Speed Optimization
Area Optimization
Make the right design choices to save power
Analyze implementation results, resource utilization report
Validation models to prove that implementation is correct
71
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
72
33
9/21/2011
Things to remember ….
DESIGN
Algorithm
Development
MATLAB
Simulink
Stateflow

Use Model-Based Design to provide
an integrated workflow

Speed up algorithm development
with a unified design environment

Automate manual steps in
FPGA implementation to enable
shorter iteration cycles
y

Integrate FPGA development tools to
reduce verification time
73
HDL Verification

How do yyou do HDL verification today?
y
74
34
9/21/2011
Verification Challenges:
HDL Verification

Design the Test Bench twice
–



10 – to – 1 ratio of Test bench LOC – to – Design LOC
Many stimuli-files from MATLAB
These are ideal references which require pre- and postprocessing
How to analyze results?
75
Verification Challenges:
HDL Verification
Demo: Re-Use System Level Test Bench
76
35
9/21/2011
Digital Down Converter

DDC accepts
p
– A high sample-rate passband signal (may be 50 to 100 Msps)

DDC produces
– A low sample-rate baseband signal ready for demodulation
~70 MSPS
RF
Section
~270 KSPS
Digital
Down
Converter
A/D
Conv
Demod
77
Integrated HDL Verification
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Automatic HDL
Code Generation
HDL CoCo-Simulation
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
Functional Simulation
Static Timing Analysis
Timing Simulation
FPGA Hardware
FPGA-inFPGAin-the
the--Loop
78
36
9/21/2011
Verify Handwritten HDL Vector-Based
Digital Down Converter
What is the impact of
these differences?
Difficult to analyze
simulation results
79
Co-Simulation with HDL simulators
Digital Down Converter
Re-use system level test
bench
Direct simulation link to
HDL Simulators
Flexible testbench
creation in Simulink
Automatically generated
HDL co-simulation
models
Difference is small and in
the stopband of the filter
80
37
9/21/2011
Additional Methods for Verification
HDL Verification Techniques

Co-simulation with MATLAB
– Test Bench
– Component

Generate vector based test benches for
standalone verification

FPGA-in-the-Loop
81
Integrate MATLAB Algorithm Development
Co-Simulation with MATLAB
MATLAB
Test Bench

Verify HDL against high-level
high level
MATLAB design
HDL Simulator
Component


R l
Replace
a “Broken”
“B k ” or un-finished
fi i h d
block in a full HDL test bench with a
working high level component
Test alternate algorithms for system
trade-off without developing HDL
82
38
9/21/2011
Harris Accelerates Verification of Signal
Processing FPGAs
Ch
Challenge
ll
Streamline a time-consuming manual process for
testing signal processing FPGA implementation
Harris FPGA-based system.
Solution
Use EDA Simulator Link to verify the HDL design
from within MATLAB
“EDA Simulator Link enabled us to
Results
development time by providing a direct
 Functional verification time cut by more than 85%
 100% of planned test cases completed
 Design implemented defect-free
greatly reduce functional verification
cosimulation interface between our
MATLAB model and our logic simulator.
As a result, we verified our design
earlier, identified problems faster,
completed more tests, and compressed
our entire development cycle.”
Jason Plew
Harris Corporation
Link to user story
83
Collaborate with Other Design Teams
Test Benches for Standalone Verification
Compile and simulation
scripts are provided
Automatically generate
self-checking test
benches
Can be used in any HDL
Simulator
84
39
9/21/2011
Challenges:
Testing algorithms on real hardware



Motivation:
building confidence
But …… interfaces with p
peripherals
p
& rest of the system needed
Difficult to construct testbenches in
real hardware
Demo: Re-Use System Level test bench
86
FPGA-in-the-Loop verification
Digital Down Converter
Integration with FPGA
development boards
Automatic creation of
FPGA-in-the-Loop
verification models
87
40
9/21/2011
FPGA-in-the-Loop verification
Digital Down Converter
Re-use system level test
bench for FPGA
verification
Flexible testbench
creation in Simulink
Building confidence that
the design works on real
hardware
88
Summary: Verification


Integration of FPGA development tools enhances
verification
–
Improved analysis, flexible testbench creation
(multi domain, feedback loops)
–
Integration with HDL verification
–
Integration with FPGA verification
A tomation gi
Automation
gives
es shorter iteration ccycles
cles
– Automatically generated verification models for:

HDL Co-Simulation

FPGA-in-the-Loop
– Wizards for legacy HDL code
89
41
9/21/2011
From Algorithm to FPGA Implementation
MATLAB® and Simulink®
Algorithm and System Design
Model Refinement for Hardware
Simulink HDL Coder
RTL Creation
EDA Simulator Link
ModelSim
HDL Co
Co--Simulation
Behavioral Simulation
Back Annotation
Implement Design
Synthesis
Map
Place & Route
Verification
Functional Simulation
Static Timing Analysis
Timing Simulation
EDA Simulator Link
FPGA--inFPGA
in-thethe-Loop
90
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
91
42
9/21/2011
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
92
Agenda
9:45
Welcome
10:00
Reduce FPGA Development
p
Time with Model-Based Design
g
11:00
Break
11:15
Integrated HDL Verification
12:00
Xilinx Target-optimized FPGA Design Using MATLAB and Simulink
13:15
Lunch
14:15
FPGA Design Optimization Techniques
15:45
Q&A, Summary and Wrap-up
93
43
9/21/2011
Things to remember ….
DESIGN
Algorithm
Development
MATLAB
Simulink
Stateflow

Use Model-Based Design to provide
an integrated workflow

Speed up algorithm development
with a unified design environment

Automate manual steps in
FPGA implementation to enable
shorter iteration cycles
y

Integrate FPGA development tools to
reduce verification time
94
ROI: Customer Adoption Of Model-Based Design
Time spent on FPGA/ASIC implementation



Shorter implementation time by 48% (total project 33%)
Reduced FPGA prototype development schedule by 47%
Shorter design iteration cycle by 80%
1st FPGA Prototype
2nd FPGA Prototype
1st FPGA Prototype
95
44
9/21/2011
How to adopt MathWorks technologies?


MathWorks tools p
provide a technology
gy to speed
p
up
p
development
MathWorks services provide the support to roll out this
technology in your organization
96
Example MathWorks Services

MathWorks Training
– Private training “Simulink HDL Coder”
– Public training “Signal
Signal Processing with MATLAB/Simulink
MATLAB/Simulink”
Fundamental trainings for uniform knowledge, quick ramp up

MathWorks Consulting
– Jumpstart service to get you up and running quickly with
Simulink HDL Coder
– Advisory service for ongoing expert advice during technology adoption
– Based on industry experience, assistance with tailoring workflow
– On site expert customization / optimization of your workflow

Technical Support
– Comprehensive, product-specific Web support resources
– 70% cases solved within 24 hours
– Included in Software Maintenance Service
97
45
9/21/2011
Were Your Expectations Met?

Please complete and return seminar survey
forms

Your comments and feedback are very
important to us
99
Next Steps …
1.
2.
Visit www.mathworks.com/fpga
pg
for more information
Visit www.xilinx.com/dsp
for more information
3.
Watch our FPGA webinars:
– www.mathworks.com/company/events/webinars
th
k
/
/
t / bi
4.
Contact your local sales reps for a trial of our FPGA tools
Questions?
100
46