Multimodal retrieval-augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where models should effectively integrate additional knowledge to generate a response. However, existing vision and language models (VLMs) are not inherently designed for context-augmented generation, limiting their effectiveness in such tasks. While synthetic data generation has recently gained attention for training large VLMs, its application for context-augmented generation remains underexplored. To address this gap, we introduce SKVQA, a large-scale synthetic multimodal dataset containing over 2 million visual question-answer pairs, each associated with external knowledge sources to determine the final answer. Compared to previous datasets, SKVQA exhibits 11× more unique questions, greater domain diversity, and a broader spectrum of image sources. Through human evaluations, we confirm the high quality of the generated question-answer pairs and their contextual relevance. Extensive experiments show that SKVQA serves both as a challenging benchmark for knowledge-based VQA and as an effective training resource for adapting generative multimodal models to context-augmented generation. Our results further indicate that models trained on SKVQA demonstrate enhanced generalization in both context-aware VQA and multimodal RAG settings.

With the coming era of Big Data, hardware implementation of machine learning has become attractive for many applications, such as real-time object recognition and face recognition. The implementation of machine learning algorithms needs intensive memory access, and SRAM is critical for the overall performance. This paper proposes a new design of high speed SRAM for machine learning purposes. With fast access time (cycle time: 650 ps, access time: 350 ps), low sensitivity to temperature variation and high configurability (less than 10% performance difference between 125_rcw_tt vs 0_rcw_tt), the proposed SRAM is a better candidate for hardware machine learning system than the conventional SRAM. Compared with Samsung HL 152, our design has smaller size (121×43 um2 vs 127×44 um2) with half the number of pins ports (12 vs 25) and higher speed (2.2GHz vs 0.8GHz).