TT-DSC: Enhancing YOLO for Marine Ecosystem through Efficient Tensor Train-based Depthwise Separable Deep Neural Network

The current era of Artificial Intelligence (AI) has witnessed significant and continuous advancements based on the powerful learning capabilities of Deep Neural Networks (DNNs), particularly those featuring convolutional (CONV) layers. In the field of marine ecosystem conservation, these advancements have revolutionized our ability to monitor and protect ocean environments. DNNs, especially those utilizing YOLO (You Only Look Once) architecture, have been instrumental in tasks such as real-time marine species identification, tracking of marine mammal migrations, detection of coral bleaching events, and monitoring of illegal fishing activities. These AI-powered tools provide unprecedented insights into marine ecosystems, enabling more timely and effective conservation actions. As we aim further to enhance the computational and storage efficiency of these networks, Tensor Train (TT) decomposition has emerged as a notable compression technique due to its high compression ratio and ability to maintain strong performance. However, the CONV layer in TT format still incurs substantial computational costs, stemming from convolution calculations and the additional multiplication operations intrinsic to TT usage. Consequently, reducing these computational costs is critical to improving the effectiveness of DNNs. To advance the computational efficiency of DNNs, this paper introduces a novel separable TT decomposition that offers an efficient TT-format CONV layer using depthwise separable convolution. Remarkably, this method not only reduces computation costs significantly but also maintains a similar capacity for parameter compression and accuracy compared to the conventional TT-format model. Furthermore, our method facilitates distributed learning based on the factorization of CONV layers. By scheduling the smaller-factored weight tensors, we significantly mitigate the GPU memory requirements of the larger model, thereby enhancing the availability and speed of training. The primary contributions of this paper are twofold: (1) a simultaneous reduction in computational cost and parameter count in TT-based CONV layers, achieved by minimizing TT redundancy and optimizing convolution, leading to up to 7–10× improvements per layer, an overall one-third reduction in parameters, and 15% reduction in FLOPs at the model level. 2) We demonstrate how our approach enables effective distributed learning and resource allocation. By merging TT decomposition and depthwise separable convolution, we present TTDSC, a TT-based depthwise separable convolution approach. This study opens new avenues to improve the efficiency of CONV. layers compression and has significant implications for large-scale deep learning applications.