OpenCL Code

OpenCL code, a framework for parallel computing on heterogeneous platforms, is a focus of ongoing research aimed at optimizing performance and efficiency across diverse hardware architectures. Current efforts concentrate on automating code tuning through techniques like multimodal deep learning and graph neural networks, improving performance for applications such as deep learning inference by employing mixed-precision and optimized kernel designs, particularly on FPGAs. These advancements are significant for accelerating computationally intensive tasks in various fields, including data mining, machine learning, and high-performance computing, especially on resource-constrained devices.