Jiwon Yoo

Ilhuan Choi, Jiwon Yoo, Yoona Lee et al.

Write-and-verify (WV) is essential for programming multi-level RRAM weights, yet under scaled-voltage and low-SNR conditions the verify read increasingly limits mapping accuracy, convergence speed and energy. We propose a Hadamard-domain WV framework that improves verify reliability without adding analog hardware. % without introducing additional analog blocks % while leveraging the existing analog front-end \emph{HD-PV} (Hadamard-Encoded Parallel-Verify) replaces conventional one-hot verify reads with $N$ orthogonal Hadamard patterns for an $N$-cell column. Changing the read basis without increasing the column-level read count, inverse Hadamard decoding reduces uncorrelated read-noise variance by a factor of $N$ and cancels common-mode disturbances. \emph{HARP} (Hadamard-based ADC-Energy-Reduced Parallel-Verify) further exploits the fact that WV needs only ternary update decisions, not full digital codes, and replaces SAR conversions with lightweight compare-only operations. Across CIFAR-10, CIFAR-100, and keyword spotting under severe read noise, conventional WV loses over 20\,\% accuracy on CIFAR-10, while HD-PV and HARP limit the loss to 0.6\,\% and 1\,\% under the same memory footprint. Compared to conventional multi-read averaging for noise reduction, HD-PV and HARP achieve comparable accuracy with up to $6.1\times$ and $3.5\times$ lower latency and $6.2\times$ and $9.5\times$ better energy efficiency, respectively. To the best of our knowledge, this is the first application of Hadamard-encoded verification to RRAM WV.

Hyeono Jung, Jangwon Lee, Jiwon Yoo et al.

Within the domain of person re-identification (ReID), partial ReID methods are considered mainstream, aiming to measure feature distances through comparisons of body parts between samples. However, in practice, previous methods often lack sufficient awareness of anatomical aspect of body parts, resulting in the failure to capture features of the same body parts across different samples. To address this issue, we introduce \textbf{Part Aware Transformer (PAFormer)}, a pose estimation based ReID model which can perform precise part-to-part comparison. In order to inject part awareness to pose tokens, we introduce learnable parameters called `pose token' which estimate the correlation between each body part and partial regions of the image. Notably, at inference phase, PAFormer operates without additional modules related to body part localization, which is commonly used in previous ReID methodologies leveraging pose estimation models. Additionally, leveraging the enhanced awareness of body parts, PAFormer suggests the use of a learning-based visibility predictor to estimate the degree of occlusion for each body part. Also, we introduce a teacher forcing technique using ground truth visibility scores which enables PAFormer to be trained only with visible parts. A set of extensive experiments show that our method outperforms existing approaches on well-known ReID benchmark datasets.

Jiwon Yoo, Jangwon Lee, Gyeonghwan Kim

Multi-scale architecture, including hierarchical vision transformer, has been commonly applied to high-resolution semantic segmentation to deal with computational complexity with minimum performance loss. In this paper, we propose a novel decoding scheme for semantic segmentation in this regard, which takes multi-level features from the encoder with multi-scale architecture. The decoding scheme based on a multi-level vision transformer aims to achieve not only reduced computational expense but also higher segmentation accuracy, by introducing successive cross-attention in aggregation of the multi-level features. Furthermore, a way to enhance the multi-level features by the aggregated semantics is proposed. The effort is focused on maintaining the contextual consistency from the perspective of attention allocation and brings improved performance with significantly lower computational cost. Set of experiments on popular datasets demonstrates superiority of the proposed scheme to the state-of-the-art semantic segmentation models in terms of computational cost without loss of accuracy, and extensive ablation studies prove the effectiveness of ideas proposed.