Dear Colleagues,

In the last decade, machine learning has emerged as an essential tool for a tremendous number of applications, such as computer vision, medicine, fintech, autonomous systems, speech recognition, and many others. Machine learning models offer state-of-the-art accuracy and robustness in many applications. The increasing deployment of machine learning algorithms introduces major computational challenges due to the growing amount of data, and due to the major growth in their model size and complexity. With the current challenges, the need for processing performance and throughput is significantly intensified. General-purpose CPUs that are designed for diverse applications cannot effectively satisfy the growing processing needs of machine learning applications. While general-purpose CPUs struggle when operating on a large amount of data, general-purpose graphics processing units (GP-GPUs) have evolved to handle a massive degree of parallel computations using thousands of relatively small processing cores. Thereby, GPUs can offer machine learning applications several orders of magnitude speedup over traditional CPUs. GPUs also introduce large memory bandwidth to satisfy the dataflow processing needs. These properties make GPUs ideal platforms to deal with the computational challenges of machine learning applications.

This Special Issue is looking for novel developments in GPU-based applications in machine learning such as:

Machine learning applications using GPUs in domains such as autonomous driving, healthcare, medical imaging, fighting disease, drug discovery, environmental science, climate science, and more;
Tiny-GPU machine learning applications;
High-performance computing applications;
IoT and edge devices;
Cloud computing;
Computational optimization for machine learning using GPUs;
Machine learning software libraries for GPUs;
Simulation and modeling of GPUs for machine learning applications.
Prof. Dr. Freddy Gabbay
Guest Editor