cmsis-dsp-integration

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Use 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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Skill Content

# 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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