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The C7x DSP cores in AM67A Series SoCs are optimized for high-performance signal and image processing tasks. These cores can be used across a wide range of applications, from low-level to advanced, through optimized libraries provided by Texas Instruments.

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DSPLIB

DSPLIB (Digital Signal Processing Library) is a library that provides high efficiency for low-level signal processing functions. Basic DSP algorithms such as FFT, FIR/IIR filtering, correlation, convolution, and matrix operations can be performed with hardware acceleration through this library. These functions run at high speeds by leveraging the parallel processing capacity of the C7 cores and provide significant performance gains, especially in image pre-processing stages (e.g., noise reduction, signal filtering). You can access the DSPLIB User Guide here.

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VXLIB

VXLIB (Vision Acceleration Library) is a library containing low-level image processing functions, compatible with the OpenVX standard. Basic image processing steps such as edge detection, histogram calculation, morphological operations, and color space conversions can be performed optimally with this library. When used together with DSPLIB, VXLIB provides high efficiency in all stages of the image processing pipeline (pre-processing, feature extraction, segmentation). You can access the VXLIB User Guide here.

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MMALIB

MMALIB (Matrix Multiply and Linear Algebra Library) provides optimized functions for convolutional neural networks (CNN), linear algebra (LINALG), and advanced digital signal processing (DSP) operations. This library enables maximum utilization of the hardware potential of the C7 cores, especially in deep learning-based image processing applications (e.g., object recognition, classification, segmentation). You can access the MMALIB User Guide here.

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Potential Use Cases

When these three libraries are used together, the C7 cores on the AM67A SoC can form the basis not only for low-level processing but also for advanced applications such as complex image analytics, machine learning inference operations, and real-time image optimization. For example:
  • Signal pre-processing with DSPLIB,
  • Image-based feature extraction with VXLIB,
  • Neural network-based classification operations with MMALIB can be applied sequentially in the same processing pipeline.
This way, while the overall system load on the CPU is reduced, energy-efficient and real-time capable image processing and mathematical computation solutions can be developed.