Nowadays, Polyhedral and Halide techniques optimize complex nested loops in deep learning frameworks’ basic operator compilation process. However, faced with many algorithm platforms, a single framework cannot be used for deployment purposes. In some ...

In this study, we investigate how to use a Domain-Specific Language—Halide to accelerate and optimize OpenVX graphs. Halide is a new high-level image processing pipeline language. It offers developers to separate the program into algorithms and ...

In recent years, as many social media and AI-enabled applications have become increasingly ubiquitous, camera-centric applications have emerged as the most popular category of apps on mobile phones. A programmer can develop a camera application in ...

Separating algorithms from their computation schedule has become a de facto solution to tackle the challenges of developing high performance code on modern heterogeneous architectures. Common approaches include Domain-specific languages (DSLs) ...

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of ...

• Pathology image analysis is computed intensive due to the high-resolution images.

• IDCT and FDCT written in Halide.
• 200 Lines of Halide replace over 20,000 lines ...

The Discrete Cosine Transform (DCT) is commonly used for image and video coding and very efficient implementations of the forward and inverse transforms are of great importance. The popular libjpeg-turbo library contains handwritten, ...

We present a new algorithm to quickly generate high-performance GPU implementations of complex imaging and vision pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels. ...

The Halide DSL and compiler have enabled high-performance code generation for image processing pipelines targeting heterogeneous architectures through the separation of algorithmic description and optimization schedule. However, automatic schedule ...

Tensor Cores (TCUs) are specialized units first introduced by NVIDIA in the Volta microarchitecture in order to accelerate matrix multiplications for deep learning and linear algebra workloads. While these units have proved to be capable of providing ...

Machine learning nowadays is profoundly impacting every aspect of our lives. With the evolution of the machine learning, many techniques, such as deep learning, improve the accuracy and performance of machine learning. Deep learning is a set of ML ...

Halide and OpenCL now play important roles for heterogeneous multi-core computing. OpenCL provides vendor-level support and Halide provides domain-specific support such as vision processing and AI model (TVM Halide IR). Halide also provides flexible ...

Efficient code generation for image processing applications continues to pose a challenge in a domain where high performance is often necessary to meet real-time constraints. The inherently complex structure found in most image-processing pipelines, the ...

New programming models have been developed to embrace contemporary heterogeneous machines, each of which may contain several types of processors, e.g., CPUs, GPUs, FPGAs and ASICs. Unlike the conventional ones, which use separate programming schemes for ...

Memory-bound applications heavily depend on the bandwidth of the system in order to achieve high performance. Improving temporal and/or spatial locality through loop transformations is a common way of mitigating this dependency. However, choosing the ...

Recently, heterogeneous computing that incorporates the main processor(s) with accelerator(s) for boosting the performance of applications becomes popular. While joining forces of the accelerators could help improve performance, it may also sometimes ...

Specialized Digital Signal Processors (DSPs), which can be found in a wide range of modern devices, play an important role in power-efficient, high-performance image processing. Applications including camera sensor post-processing and computer vision ...