A GPU-enabled multi-frame reconstruction for 3D electrical tomography using Tensor cores

Electrical tomography (ET) has emerged as a sustainable and non-invasive imaging technique, offering nonionizing and non-radioactive solutions for various applications. Advances in hardware systems have enabled high-speed data acquisition and image reconstruction, but the computational demands of 3D ET systems with large electrode arrays and complex finite element models present significant challenges for real-time deployment, particularly in resource-constrained edge environments. This paper proposes a GPU-enabled Tensor Core-based reconstruction strategy for designing high-speed ET systems suitable for Internet-of-Things and embedded platforms. By leveraging Tensor Core units and employing a multi-frame reconstruction technique, the proposed approach demonstrates substantial performance improvements over traditional methods. Five different algorithms, encompassing both non-iterative and iterative approaches, were implemented to validate the benefits of this strategy. Experimental results on an embedded GPU achieved a speed gain of over 13 times compared to a general-purpose computer, and over four times when using Tensor Cores instead of CUDA cores alone. The system achieved frame rates of up to 3048 frames per second for a 32-electrode setup with 2 million mesh elements, highlighting its potential to meet the computational demands of sustainable, efficient, real-time, and portable ET systems.