Matlab as a Design Environment
for Wireless ASIC Design
Erik Lindskog
Beceem Communications, Inc.
[email protected]
June 16, 2005
Introduction to Beceem
 Wireless communication system developer
 Using Matlab for ASIC design
Product development phase:
 Algorithm development - Matlab
 Algorithm and system modeling - Matlab
 RTL design – Verilog
 RTL verification - SystemC, Verilog and Matlab
 SW design – C++
 SW verification – C++ and Matlab
Advantages With Matlab
 Rich support for mathematical operations and visualization
 Most common tool for signal processing research
 Graduates in communication and signal processing
engineering are typically highly familiar with Matlab
 Fewer people have experience in programming in C++
 Full fledged programming as well as scripting language
 Interpretating language – Integrates well with other programs
 Powerful debugging support
 Not a typed language - Highly ‘moldable’
Matlab Simulator Design
 Common simulator for:
 PHY
algorithm development and studies
 Small network full PHY studies of



Effect of co- and adjacent channel interference on
PHY algorithms
Synchronization and hand-over
Link-adaptation
 Fixed
point modeling
 RTL signal processing reference
 Standalone code for
 Independent
lower complexity studies
 Generation of independent references
Matlab Simulator Design, cont.
 Organization
 Common
core source code
 Simulation examples driving source code
 Single file with default parameter settings enables
developers to work reasonably independently while
using the common code
Open Source/Freeware Software
 CVS - Concurrent Version Control system
 Cygwin – Unix emulator for Windows
 CVS Tortoise – File browser based CVS client
 Integrates external free software tools like ExamDiff for
CVS diffing
 WinCVS – GUI based CVS client
Signal Processing Design Flow
 Module design
 In
standalone code, or
 Direct in main simulator
 Floating point system modeling
 Integration
of modules into main simulator
 Floating point performance simulations
Signal Processing Design Flow, cont.
 Fixed point module design
 In
module design environment to excite corner cases
 Integrated in main simulator to get realistic
signal levels
 Fixed point system modeling
 Full
system fixed performance simulations
 Ability to turn fixed point on/off per block
 Analysis of quantization statistics to
pin-point problems
Matlab – RTL Design Flow
 Matlab fixed point design handed over to RTL
 Module verification
 File-in-file-out
test vector comparison between
MATLAB and Verilog
 Extensive testing to cover corner cases in
fixed point arithmetic
 Random signals to guarantee fixed point equivalence
Matlab – RTL Design Flow
 System testing
 Side-by-side
testing of Verilog and MATLAB in
SystemC testbench
 MATLAB simulator compiled and linked to C-program
called from SystemC testbench
 Verilog code excited from SystemC testbench
 Verilog simulation speed is the limiting factor
 Common test for Matlab and Verilog code
 Directed
testing for initial coverage
 Random testing for extensive coverage
 All system tests part of Matlab automated
regression testing
Matlab – SW Design Flow
 Matlab fixed point design handed over to SW
 Module verification
 File-in-file-out
test vector comparison between
MATLAB and C++ SW code
 Simulation example test cases in Matlab for
C++ SW code
 System testing
 Side-by-side
testing of SW on FPGA and
Matlab system simulation
Fixed Point Modeling
 Currently performed in regular Matlab
 Regular arithmetic operations where precision is
allowed to grow
 Quantization with quantization functions where
precision is to be limited
 Automatic fixed point statistics collection
Object Oriented Fixed Point Modeling
 Simultaneous floating and fixed point
 Fixed point format embedded in variable
 Matlab object oriented support limited
operator cannot be overloaded –
Traditional quantization with assignment operation in
C++ cannot be done
 Function overloading only sensitive to first argument
 Assignment
 Catalytic have an object oriented fixed point
modeling tool
 Matlab also now have an object oriented fixed point
toolbox
Fixed Point Analysis
 Analysis of fixed point quantization statistics
– E.g. headroom above 67th percentile
 Signal to quantization noise ratio – SQNR
 Over- and underflow ratios
 Signal level histogram
 Headroom
 Full system simulation fixed point analysis
 Example Matlab implementation
 Fixed
point analysis function
 Example call: fxp_stat(x, 2, 5)
 Store statistics in function call stack tree structure
 Analyze statistics after completed simulation
 Block-swapping analysis
 Switching
of fixed and floating point blocks
Lab use of Matlab Simulator
 Modeling of Tx or Rx part of modem against HW Rx
or Tx modem
 Interoperability testing with signal generators and
signal analyzers
 MAC-PHY interoperability testing
 Simple RF testing
 Over the air transmission demonstration when
combined with RF
Matlab Regression Testing
 Developer regression testing
 To
many tests to run all
 Directed and/or randomly selected set of tests.
 Automated Regression Testing
 All
regression tests checked for each block of commits
 Breaking commit identified for each failing
regression test
 Result of testing automatically emailed to developer
 High SNR Tx-Rx tests
 Single SNR BER tests
 Simulation example execution tests
 RTL verification tests mimicing exact use in
RTL test bench
Matlab Regression Testing, cont.
 Automated Random Testing
 Script
generating random traffic for testing of Matlab
simulator and RTL
 Periodic random testing of code in repository with
random Tx-Rx tests
 Result automatically emailed to developers
 Provides extensive coverage
 Scripting language - Matlab
Speeding up Matlab Code
 Scalar loops
 Loop
over scalar operation are slow
 Vectorization
 Convert
scalar loops to matrix operations
 Conversion to C-code
 Compilation of Matlab
 Removes
the need for other language
 Limits the need for vectorization of code
 Limits duplication of code in Matlab and C
 Example compiler: Catalytic Matlab Compiler
Summary
Matlab
 Powerful tool for signal processing design
 Lessens ‘distance’ between algorithms and HW
 Facilitates for algorithm designers to be closely
involved in fixed point testing and RTL verification
 Interacts easily with other programs and equipment
 Attractive design environment for ASIC design