ARM Compute Library now publicly available
The library is now available free of charge under a permissive MIT open-source license. It initially contains a large number of functions implemented for the Cortex-A family of CPUs and for the Midgard and Bifrost families of Mali GPUs: a repository of low-level, optimized software functions that developers can source individually or use as part of complex pipelines in order to accelerate their algorithms and applications [image; ARM website].
Included in the ARM Compute Library, in its first release, is a comprehensive set of functions which have been built over years of experience working with ARM’s partners and developers around imaging and vision based products, as well as the company’s experience optimizing machine learning frameworks such as Google TensorFlow.
The libraries include the following categories of functions:
Basic arithmetic, mathematical and binary operator functions
Colour 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)
Apart, ARM says, from being a comprehensive, one-stop solution for common CV and ML performance optimized routines, an important characteristic of the ARM Compute Library is portability. The CPU functions are implemented using NEON intrinsics, which enables developers to re-compile them for their target architecture. This means the code is transferable between ARMv7 or ARMv8 processor implementations and can be compiled for 32-bit and 64-bit. The GPU version of the library consists of kernel programs written using the OpenCL standard API, which again is portable across multiple processors and architectures (albeit specifically optimized for ARM Mali GPUs).
The library functions can be used to accelerate key stages of computer vision and machine learning pipelines. The library is operating system agnostic and has already been deployed on a broad variety of modern Linux and Android ARM-based system-on-chip platforms.
It is, ARM continues, a useful tool that can significantly reduce cost and effort for developers targeting imaging, vision and machine learning applications – enabling them to focus on differentiation and reduce their products’ time to market. The ARM Compute library is mature and tested, has been utilised by several consumer and mobile silicon vendors and OEMs inside their products, as well as many ISVs across the globe.
More in this ARM post; here
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