ARM-software/CMSIS-DSP

Health v2 · maintenance only

Adoption not part of the score

1106 stars · 246 forks observed · 2026-10-07

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CMSIS-DSP is ARM's optimized embedded compute library providing DSP and math kernels for Cortex-M and Cortex-A processors, including FFT, filtering, matrix operations, statistics, and classical ML kernels. It supports multiple datatypes (f64/f32/f16/q31/q15/q7) with vectorized variants for Helium and Neon, plus a NumPy-compatible Python wrapper for prototyping.

Use cases

• implement FFT on a Cortex-M microcontroller
• run digital filters on embedded ARM hardware
• do matrix math with fixed-point arithmetic on MCU
• deploy classical ML like SVM on embedded devices
• prototype DSP algorithms in Python then port to C
• compute MFCC features on microcontroller
• optimize math kernels with Neon on Cortex-A

When to choose

• you're developing on ARM Cortex-M or Cortex-A and need optimized math/DSP kernels
• you need fixed-point (q7/q15/q31) or half-float support for resource-constrained MCUs
• you want a Python prototyping path that maps closely to the C API
• you need vectorized performance via Helium or Neon

When to avoid

• you're targeting non-ARM platforms like x86 or RISC-V
• you need general-purpose desktop/server math libraries like NumPy or Eigen
• you need deep learning inference rather than classical DSP/ML kernels

Facets

library · maturity stable

machine-learning math image-processing audio-processing sdk embedded-systems machine-learning embedded cpp python cross-platform dsp fft digital-signal-processing fixed-point-arithmetic arm-cortex-m arm-cortex-a neon helium filters matrix-math cmsis microcontrollers numpy algorithms signal-processing

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