This paper proposes a hardware-efficient INT8 training framework with dual-scale adaptive gradient quantization (DAGQ) to cope with the growing need for efficient on-device CNN training. DAGQ captures both small- and large-magnitude gradients, ensuring robust low-bit training with minimal quantization error. Additionally, to reduce the computational and memory demands of stochastic rounding in low-bit training, we introduce a reusable LFSR-based stochastic rounding unit (RLSRU), which efficiently generates and reuses random numbers, minimizing hardware complexity. The proposed framework achieves stable INT8 training across various networks with minimal accuracy loss while being implementable on RTL-based hardware accelerators, making it well-suited for resource-constrained environments.

Instance segmentation plays a critical role in high-precision computer vision applications, such as autonomous driving and medical image analysis. As demands for both model accuracy and privacy-preserving solutions continue to rise, on-device training is gaining traction for enabling secure, adaptive learning directly on edge devices. However, the intensive computation and complex dataflows inherent to instance segmentation models pose a major barrier to achieving real-time training in resource-constrained environments. While prior research on on-device training has focused largely on image classification, efficient hardware acceleration for instance segmentation training remains largely unexplored. In this work, we present TETRIS, the first FPGA-based hardware accelerator specifically tailored for training instance segmentation models. TETRIS introduces structural and quantization-aware optimizations, including channel-aware smoothing quantization and distribution-aware tie quantization, to significantly reduce both computational and memory overhead. Moreover, TETRIS adopts a heterogeneous hardware architecture, incorporating a reconfigurable convolution processing unit (RC-PU) that supports variable kernel sizes and a fully pipelined auxiliary processing unit to handle specialized operations efficiently. Experimental evaluation on YOLACT-based instance segmentation tasks demonstrates that TETRIS achieves a peak performance of 805.9 GOPS and an energy efficiency of 69.5 GOPS/W, confirming its ability to support real-time training even in resource-constrained edge environments.

With the ease of accessing large unlabeled datasets, studies on semi-supervised learning for object detection (SSOD) have become increasingly popular. Among these SSOD studies, the pseudo-labeling method significantly depends on the accuracy of the pseudo-labels; thus, inaccurate annotations must be filtered to prevent performance degradation. This study classifies annotation errors that occur in pseudo-labeling methods as false negative (FN) and false positive (FP), and solutions to address each type of error are proposed using uncertainty information obtained through Gaussian modeling. Network performance is improved by preventing the background learning of the FN objects based on the uncertainty of the network output. In addition, based on the uncertainty of the annotations, low-reliability annotations are filtered out, and the learning reflectivity of FP objects is determined. Considering the network performance improvement and training complexity, the proposed method employs one-phase learning, including a single pseudo-label update, to achieve maximum performance with the minimum learning process. Moreover, an algorithm is proposed for an optimal update point search to increase the expected performance improvement. Experiments on the Pascal VOC, COCO, and Cityscapes datasets show that the SSD network improves accuracy by 3.3%, 4.7%, and 4.1%, respectively, with negligible computational complexity compared to the baseline.