The rapid advancements in Artificial Intelligence (AI) increasingly underscore the critical importance of multi-modal datasets for training robust and versatile models. However, the dominance of English-centric resources and the scarcity of datasets capturing diverse languages and cultural nuances limit AI's inclusivity and global applicability, posing a key challenge. Critically, language's intrinsic link to cultural context demands models possess profound cultural insight beyond mere translation to genuinely grasp societal norms and unique cultural expressions. Culturally rich broadcast content offers a solution; its systematic curation can mitigate linguistic/cultural imbalances, fostering culturally deep multi-modal datasets. This paper introduces the HAN (Korean Heritage Augmented Narrative Visual-Language Description) dataset, a new resource for multilingual image captioning and retrieval. HAN comprises 41,000 images captured from Korean broadcast video clips with 410,000 Korean/English narrative-style captions offering multifaceted perspectives on each visual instance. By incorporating Korean heritage, HAN reflects cultural diversity, addressing limitations of existing datasets focused on simple descriptions. Furthermore, this work analyzes HAN's caption diversity impact on retrieval, proposing strategies to enhance efficacy. These findings underscore HAN's potential to significantly advance multi-modal and multilingual processing, supported by its value as a rich resource for learning approaches that integrate diverse data types (like vision and text), natural language processing across various languages, and Korean heritage studies.

Binarized Neural Networks (BNN) has shown a capability of performing various classification tasks while taking advantage of computational simplicity and memory saving. The problem with BNN, however, is a low accuracy on large convolutional neural networks (CNN). Local Binary Convolutional Neural Network (LBCNN) compensates accuracy loss of BNN by using standard convolutional layer together with binary convolutional layer and can achieve as high accuracy as standard AlexNet CNN. For the first time we propose FPGA hardware design architecture of LBCNN and address its unique challenges. We present performance and resource usage predictor along with design space exploration framework. Our architecture on LBCNN AlexNet shows 76.6% higher performance in terms of GOPS, 2.6X and 2.7X higher performance density in terms of GOPS/Slice, and GOPS/DSP compared to previous FPGA implementation of standard AlexNet CNN.

Stochastic computing (SC) is a promising computing paradigm for applications with low precision requirement, stringent cost and power restriction. One known problem with SC, however, is the low accuracy especially with multiplication. In this paper we propose a simple, yet very effective solution to the low-accuracy SC-multiplication problem, which is critical in many applications such as deep neural networks (DNNs). Our solution is based on an old concept of sign-magnitude, which, when applied to SC, has unique advantages. Our experimental results using multiple DNN applications demonstrate that our technique can improve the efficiency of SC-based DNNs by about 32X in terms of latency over using bipolar SC, with very little area overhead (about 1%).

Stochastic computing (SC) is a promising approach for low-power and low-cost applications with the added benefit of high error tolerance. However, the high overhead of generating stochastic bitstreams can offset the advantages of SC especially when a large number of bitstreams are needed. In this paper, we propose a new stochastic number generator (SNG) that significantly reduces area and energy while improving accuracy. Experimental results show that the proposed SNG can reduce energy by more than 72% compared with the state-of-the-art designs.