Day 3: Model-Based Design with Simulink: Code Gen and V&V

Code Generation and Targeting - Pete Szpak

Maximizing Component Reuse in Code Generation - Xiaocang Lin

• Improving encapsulation in generated code

  • Minimize public interface dependecny (R2017a)

  • Keep local function truly local (R2017b)

    • Eliminates name collisions

    • cset: Preserve static keyword in function declarations

• Simulink Library -> Reusable Code Parts

  • Reuse a function within a model (since 2002)

  • Reuse a function across multiple models (since R2012b)

Responding to the Ever-Increasing Demands on Code Efficiency - Partha Biswas

• Traditional MBD Workflow in pure control applications

  • RAM and Data minimization/efficiency is critical

• Increasing design complexity (i.e. vision application domain)

  • High demand for performance

    • Zero tolerance for unnecessary copies

  • Tareting heterogeneous platforms

    • Leverage concurrency

• Improvements

  • No unnecessary data copies for bus element assignment (R2017a)

    • TODO: Investigate

  • Favor buffer reuse in a chain of blocks (R2017b)

    • 11% RAM reduction

  • RAM and Data Copy Reduction (R2017b)

    • Reusable CSC

    • Inout arguments in MATLAB/Simulink function & MATLAB system blocks

  • Exploiting parallelism

    • Data-level parallelism

    • Task-level parallelism

Reusing Generated Code Across MATLAB Releases - Jon Fielder

• Addressing the challenge of manual code integration

  • Difficulties

    • Duplicate utilities across releases

    • Non-functional changes

    • Perhaps a large number of utilities to handle

  • Approaches

    • Reduce number of utilities generated (R2016b)

  • Automatically integrate shared code (R2016b)

    • Associate model components with existing shared code

  • Automatically integrate model code (R2016b)

    • Directly import model code and shared code

      • Utilizes SIL and PIL using the previously generated and verified code

        • As far back as R2010a

    • Cross-relese system simulation

  • Generate efficient integrated code (R2016b)

    • Call to the previous releases are fully inline, without wrapper code

  • Easily reuse model reference components

    • Import model refreence component (R2017a)

    • Import model reference hierearchy (R2017b)

Real-Time Simulation and Testing - Marc Schafer

• Simulink Desktop Real-Time integration to ThingSpeak (R2017b)

• Logging improvements (R2017b)

  • Works just like you would for desktop simulations

• Simulink Test and Simulink Real-Time can interoperate

• Baseline Real-Time Target Machine (R2017a)

• Mission: If you know how to use Simulink, you know how to use Simulink Real-Time

Advances in C++ Code Generation - Pengcheng Wu

• An object-oriented, C++ code realization is more natural sometimes

• Separation of concerns

  • Publish-Subscribe

  • Discovery

  • Communication

  • Quality of Service

• Since R2016a, more production code use-cases

• Multi-instantiable Simulink Functions: Natural way to map models to classes (R2017b)

  • Can utilizie dot notation, similar to C++ code

Advances in HDL Code Generation - Kiran Kintali

• Advanced algorithms require hardware acceleartion

• FPGA/SoC are key to meet the growing performance needs

• HDL Code Generation workflows help manage complexity

• Hardships of fixed-point conversion (with high dynamic range math models)

  • New support for floating-point and fixed-point algorithms

    • Directly target floating-point on the hardware

      • Misconception that fixed-point is always more efficient than floating-point

• Math more accessible for FPGA

  • Single (R2016a)

  • Double (R2017b)

Generating GPU Code for Acceleration - Girish Venkataramani

• New Product in R2017b

  • GPU Coder

    • Take MATLAB and generate CUDA code for NVIDIA GPUs

    • Accelerated implementation of parallel algorithms on GPUs

      • Neural Networks

        • Deep Neural Network

      • Image processing and computer vision

      • Signal processing and communications

Autonomous Systems Design - Don Orofino

• Robotic Autonmous Systems are a MEGATREND

  • Fixed-location robotics (eg, robotic arm)

  • Wheeled vehicles

  • UAV/Drone/Quadcopters

• Automated driving is a high priority for TMW

  • Directing all possible development teams to this area

Delivering Autonomous Systems using Model-Based Design - David Koh

• Need to combine many diverse technologies

• Managing complexity is a key to developing autonomous systems

  • High-level abstraction using MATLAB, Simulink, & Stateflow

  • Parallel Simulations (R2017a)

    • parsim

      • Multicore

      • Data center on the cloud

  • Code generation

    • C/C++/HDL/PLC generators

  • GPU Coder (R2017b)

    • Translate MATLAB to optimized CUDA

  • Closed-loop testing & real time hardware-in-the-loop

    • Vehicle blockset toolbox (R2017b)

Automated Driving - Witek Jachimczyk

• Automated Driving System Toolbox (R2017a)

  • Emphasis on perception and algorithm validation

    • Vehicle detection with distance estimation using a monocular camera

      • Parabolic lane boundary model

      • Bird's eye projection

    • Ground Truth Labeler

      • API for custom automation algorithm

• Direction

  • Simulink support for tracking and sensor fusion (R2017b)

Deep Learning and Computer Vision for Environment Perception - Birju Patel

• Deep Learning Algorithms

  • R2016a

    • Neural Network Toolbox

    • Computer Vision System Toolbox

  • R2017a

    • Computer vision System Toolbox

      • Train faster R-CNN Dector

    • Automated Driving System Toolbox

  • R2017b

    • Computer Vision System Toolbox

      • Semantic Segmentation

    • Automated Driving System Toolbox

      • Labeling Training Data

• Deployment

  • Code generation

    • GPU (R2017b)

Sensor Fusion and Tracking - Vincent Pellissier

• Fuse detections with multi-object tracker (R2017a)

  • Vision & radar

    • Fused detector

    • Fused tracker

• Synthesize scenario to test tracker (R2017a)

• Sensor fusion in Simulink (R2017b)

  • Adaptive cruise control example will be shipped

Localization, Mapping, and Planning - Remo Pillat

• Robotics System Toolbox

  • Plan manpulator motion (R2017a)

  • UAV localization & Control (R2017a)

Replay Simulation of Recorded Sensor Data - David Koh

• Why?

  • To develop robust algorithms, need lots of real-world data collected under varying conditions

• Robotics System Toolbox

Test and Verification - Bill Aldrich

Work with Requirements in Simulink - Yasumitsu Ito

• New interface

  • Bring in requirements into MATLAB

  • Tracing is simplified

  • RMI will still be available

  • Bi-directional linking with DOORS isn't initially supported

Use Quality Measures to Improve Your Design - Sai Avula

• Access Model Quality Metrics Dashboard (R2017b)

• Clone detection (R2017b)

  • Cloned component

    • Subsystem that matches any other subsystem in the model hierarchy

    • Convert all cloned components to library link subsystem

    • Size of generated code was reduced by 50% in one industry test model

Author Tests with Spreadsheets - Khoo Yit Phang

• Leverages Simulink Test

  • Excel file are input and baseline options (R2017b)

    • Supports boolean, enumeration, and numeric data types

    • Utilize multiple sheets to define multiple tests

    • Test results can be captured effortlessly to Excel files

Eliminate Defects Using Regression Testing - A. Abhishek

• Use Simulink Project impact analysis to identify tests to be re-run

• Focus on sandbox testing

  • Local machine testing

  • Simulink Test Manager

    • Run tests, fetch results

• Model coverage

Address Missing Coverage - Anjali Joshi

• Simulink Design Verifier

  • Detect dead logic

    • Newly integrated in coverage results explorer (R2017a)

    • Fix design to eliminate dead logic

    • Add justification for dead logic

    • Automatically add tests for missing coverage (R2017a)

• Coverage Results Explorer (R2016b)

  • TODO: Investigate

Test Across Releases - Gregg Smith

• Approaches

  • Use SIL replacement blocks to host older code in newer release (R2017b)

  • crossReleaseExport()

  • crossReleaseImport()

• Test Manager (R2017b)

  • Multi-release testing

    • Automatically spawns older releases of MATLAB and tests on those

Breakout Sessions

Streamlining Certification Workflows with Model-Based Designa - Eric Dillaber

• Model Advisor

  • Modeling Standard Checks

    • Security (eg, CERT-C) added

  • Design error detection is now exposed in Model Advisor (R2017a)

• Code coverage

  • Bill recommends running code coverage in SIL mode, then running PIL mode with code coverage disabled

    • Code coverage instruments the code

• Qual/Cert Kit Updates

  • R2017a

    • Qualification of

      • Simulink Design Error Detection

      • Polyspace Report

      • Simulink Block Execution Coverage

  • R2017b

    • Qualification of

      • New MathWorks High-Integrity Guideline Checks

      • MAAB Guideline Checks

      • MISRA Checks

    • Improvements to Simulink Coverage Qual kit

    • Guidance on Qaulifying Custom Checks

    • Menu Access to Kit artifacts, trace matrix and tests

Customizing Embedded Coder to Generate Perfect Code for Your Environment - Xiaocang Lin