SPRACW5A April   2021  – December 2021 F29H850TU , F29H859TU-Q1 , TMS320F2800132 , TMS320F2800133 , TMS320F2800135 , TMS320F2800137 , TMS320F280021 , TMS320F280021-Q1 , TMS320F280023 , TMS320F280023-Q1 , TMS320F280023C , TMS320F280025 , TMS320F280025-Q1 , TMS320F280025C , TMS320F280025C-Q1 , TMS320F280033 , TMS320F280034 , TMS320F280034-Q1 , TMS320F280036-Q1 , TMS320F280036C-Q1 , TMS320F280037 , TMS320F280037-Q1 , TMS320F280037C , TMS320F280037C-Q1 , TMS320F280038-Q1 , TMS320F280038C-Q1 , TMS320F280039 , TMS320F280039-Q1 , TMS320F280039C , TMS320F280039C-Q1 , TMS320F280040-Q1 , TMS320F280040C-Q1 , TMS320F280041 , TMS320F280041-Q1 , TMS320F280041C , TMS320F280041C-Q1 , TMS320F280045 , TMS320F280048-Q1 , TMS320F280048C-Q1 , TMS320F280049 , TMS320F280049-Q1 , TMS320F280049C , TMS320F280049C-Q1 , TMS320F28075 , TMS320F28075-Q1 , TMS320F28076 , TMS320F28374D , TMS320F28374S , TMS320F28375D , TMS320F28375S , TMS320F28375S-Q1 , TMS320F28376D , TMS320F28376S , TMS320F28377D , TMS320F28377D-EP , TMS320F28377D-Q1 , TMS320F28377S , TMS320F28377S-Q1 , TMS320F28378D , TMS320F28378S , TMS320F28379D , TMS320F28379D-Q1 , TMS320F28379S , TMS320F28384D , TMS320F28384D-Q1 , TMS320F28384S , TMS320F28384S-Q1 , TMS320F28386D , TMS320F28386D-Q1 , TMS320F28386S , TMS320F28386S-Q1 , TMS320F28388D , TMS320F28388S , TMS320F28P650DH , TMS320F28P650DK , TMS320F28P650SH , TMS320F28P650SK , TMS320F28P659DH-Q1 , TMS320F28P659DK-Q1 , TMS320F28P659SH-Q1

 

  1.   Trademarks
  2. 1Introduction
  3. 2ACI Motor Control Benchmark Application
    1. 2.1 Source Code
    2. 2.2 CCS Project for TMS320F28004x
    3. 2.3 CCS Project for TMS320F2837x
    4. 2.4 Validate Application Behavior
    5. 2.5 Benchmarking Methodology
      1. 2.5.1 Details of Benchmarking With Counters
    6. 2.6 ERAD Module for Profiling Application
  4. 3Real-time Benchmark Data Analysis
    1. 3.1 ADC Interrupt Response Latency
    2. 3.2 Peripheral Access
    3. 3.3 TMU (math enhancement) Impact
    4. 3.4 Flash Performance
    5. 3.5 Control Law Accelerator (CLA)
      1. 3.5.1 Full Signal Chain Execution on CLA
        1. 3.5.1.1 CLA ADC Interrupt Response Latency
        2. 3.5.1.2 CLA Peripheral Access
        3. 3.5.1.3 CLA Trigonometric Math Compute
      2. 3.5.2 Offloading Compute to CLA
  5. 4C2000 Value Proposition
    1. 4.1 Efficient Signal Chain Execution With Better Real-Time Response Than Higher Computational MIPS Devices
    2. 4.2 Excellent Real-Time Interrupt Response With Low Latency
    3. 4.3 Tight Peripheral Integration That Scales Applications With Large Number of Peripheral Accesses
    4. 4.4 Best in Class Trigonometric Math Engine
    5. 4.5 Versatile Performance Boosting Compute Engine (CLA)
    6. 4.6 Deterministic Execution due to Low Execution Variance
  6. 5Summary
  7. 6References
  8. 7Revision History

2.1 Source Code

The software benchmark example is available in C2000Ware.

  • C28x CPU benchmark available in C2000Ware version 3.04.00.00 onwards

  • CLA accelerator benchmark available in C2000Ware version 4.00.00.00 onwards.

The example can be found at the following location within the C2000Ware installation.

<C2000Ware>\examples\demos\benchmark\aci_motor_benchmark

The top level folders are:

  • common
  • device_support
  • f28004x
  • f2837x

The source code is located in the 'common' and 'device_support' folders. The 'common' folder contains device independent code such as transform algorithms, motor modeling code, and so forth. The 'device_support' folder contains code that is specific to the particular device such as device configuration, peripheral read/write. and so forth within sub folders named for that device.

The application is supported in Code Composer Studio™ (CCS) for TMS320F28004x and TMS320F2837x devices and can be executed on TMS320F28004x Launchpad as well as TMS320F2837x Launchpad. The respective CCS projects are located in the sub-folder 'ccs' within the 'f28004x' and 'f2837x' top level folders.

The application has multiple implementation variants; one variant uses the math engine Trigonometric Math Unit (TMU) for performing trigonometric calculations needed by the Park, Inverse Park and Flux Estimator control algorithms, the other variant uses a software library called FastRTS to perform the trigonometric calculations. The FastRTS library is included in C2000Ware, the library and documentation can be found at <C2000Ware>\libraries\math\FPUfastRTS\c28\. The goal with these two variants is to show the performance boost the math engine TMU can provide as compared to a software library based implementation.

Another set of implementation variants involve the CLA. One variant is with the C28x CPU offloading part of the compute to CLA and another variant is with the benchmarking control code executing entirely from CLA only The goal of these two variants is to show how CLA can be used to aid in meeting real-time goals.