Arm Compute Library is a collection of low-level functions optimized for Arm Cortex-A CPUs and Arm Mali GPUs, targeting image processing, computer vision, and machine learning. It is available free of charge under a permissive MIT open source license.
As a convenient repository of low-level optimized functions, developers can source requirements individually or use the functions as part of complex pipelines to accelerate algorithms and applications.
The library’s collection of functions includes:
- Basic arithmetic, mathematical, and binary operator functions
- Color manipulation (conversion, channel extraction, and more)
- Convolution filters (Sobel, Gaussian, and more)
- Canny Edge, Harris corners, optical flow, and more
- Pyramids (such as Laplacians)
- HOG (Histogram of Oriented Gradients)
- SVM (Support Vector Machines)
- H/SGEMM (Half and Single precision General Matrix Multiply)
- Convolutional Neural Networks building blocks (Activation, Convolution, Fully connected, Locally connected, Normalization, Pooling, Soft-max)
With any complex software system it is critical to understand the interworking of different modules and the capabilities of the underlying hardware. If you have any questions about software on Arm-based processors, talk to an Arm expert.
Arm NN bridges the gap between existing NN frameworks and the underlying IP. It enables efficient translation of existing neural network frameworks, such as TensorFlow and Caffe, allowing them to run efficiently – without modification – across Arm Cortex-A CPUs, and Arm Mali GPUs and the Arm Machine Learning processor.
The Cortex-A processor series is designed for devices to undertake complex compute tasks, such as hosting a rich operating system platform and supporting multiple software applications and embedded designs. Cortex-A processors power intelligent solutions, from edge to cloud, for next-generation experiences.
Mali Graphics Processors
Including both graphics and GPU Compute technology, Mali GPUs offer a diverse selection of scalable solutions for low-power to high-performance smartphones, tablets, and DTVs.