SPRACW5A April   2021  – December 2021 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 , TMS320F28P659DK-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