Recent advances in retrieval-augmented generation (RAG) highlight retrieval latency, dominated by storage access, and large embedding-index size as the major bottlenecks. Although processing-in-memory (PIM) based on 3-Dnandflash and binary passage retriever (BPR) algorithm offer potential solutions for reducing latency and memory usage, naively integrating BPR algorithm into an SSD based on prior in-memory search (IMS) architectures results in negligible performance gain with substantial hardware overhead. This stems from the fact that prior IMS architectures are optimized only for search, leaving candidate readout misaligned with thenandpage direction and requiring excessive readcycles, while BPR's original top-$L$selection further requires high-precision ADCs and sorting logic. To address these limitations, we propose an algorithm–hardware co-designed in-storage information retrieval architecture. We introduce a hardware-efficient optimized BPR algorithm with a threshold-based selection and two-stage in-storage processing approach to minimize data transfer overheads. We further present an IMS architecture with page-aligned encoding that efficiently supports both search and candidate read operations, eliminating the read cycle penalty. We also present a lightweight in situ error monitoring scheme that bypasses error-correcting code (ECC) decoding while preserving error-aware refresh triggering. Experiments show that our design reduces the amount of data read fromnandflash by 7.94–$1330.40\times $compared with host-side processing baselines and further reduces controller-to-host data transfer, while maintaining the retrieval accuracy. It also reduces total operationcycles by up to 96.93% compared with prior IMS architectures, and lowers error-detection power by 94.71% and total read energy by 17.22% compared with a conventional ECC decoder for error monitoring.

Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unseen domains. Our approach disentangles the domain-specific style and domain-invariant content encoded in higher-order statistics (i.e., feature covariance) of the feature representations and selectively removes only the style information causing domain shift. As shown in Fig. 1, our method provides reasonable predictions for (a) low-illuminated, (b) rainy, and (c) unseen structures. These types of images are not included in the training dataset, where the baseline shows a significant performance drop, contrary to ours. Being simple yet effective, our approach improves the robustness of various backbone networks without additional computational cost. We conduct extensive experiments in urban-scene segmentation and show the superiority of our approach to existing work. Our code is available at this link1.