cmsis-dsp-integration
SolidUse when integrating, configuring, or debugging CMSIS-DSP, ARM math functions, FFT, filters, fixed-point DSP, vector math, or Cortex-M signal processing
AI & Automation 22 stars
2 forks Updated 1 weeks ago MIT
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# CMSIS-DSP Integration
## Overview
Use this skill to integrate CMSIS-DSP by matching the target core, FPU/DSP extensions, data type, scaling, and buffer alignment. DSP bugs often come from numeric format and build flags rather than function calls.
## When To Use
Use this skill when:
- The user wants CMSIS-DSP or ARM math functions on Cortex-M or Cortex-A/R targets.
- The task involves FFT, FIR/IIR filters, matrix math, statistics, PID, Q15/Q31/f32, or optimized vector operations.
- Results are wrong, saturated, NaN, too slow, or differ from desktop reference output.
Do not use this skill for TinyML inference runtime integration. Use `tinymaix-integration` or ML-specific skills instead.
## First Questions
Ask for:
- MCU/core, FPU/DSP extension, compiler, and build flags.
- CMSIS-DSP version and how it is included.
- Function family and data type: f32, f16, q7, q15, q31, or mixed.
- Input range, expected output, reference data, and buffer sizes.
- Whether performance, code size, or accuracy is the primary goal.
## Integration Checklist
1. Match core flags.
Compiler options must match FPU, ABI, and DSP extensions.
1. Choose numeric format deliberately.
Fixed-point Q formats need scaling, saturation, and headroom analysis.
1. Validate buffers.
FFT/filter/state buffers must be sized and aligned as required.
1. Compare with golden vectors.
Use known input/output before live sensor data.
1. Measure performance on target.
Desktop estimates do not prove ...
Details
- Author
- easyzoom
- Repository
- easyzoom/aix-skills
- Created
- 3 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
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