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D-NOVA accelerates RAG with in-storage vector search · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed D-NOVA, a novel in-storage retrieval accelerator designed to significantly improve the performance and energy efficiency of Retrieval-Augmented Generation (RAG) systems. By embedding search functionality directly into NAND flash memory and introducing a new distance metric called Dual-Bound Tight Similarity Sensing (DTS), D-NOVA aims to overcome the latency and energy bottlenecks associated with traditional RAG architectures. This approach allows for vector search directly within the memory, leading to substantial speedups and power savings compared to CPU-based methods and existing in-storage accelerators. AI

IMPACT This in-storage acceleration could significantly reduce the computational cost and latency of RAG systems, making LLM inference more efficient and accessible.

RANK_REASON The cluster describes a research paper detailing a new hardware-software co-designed system for accelerating AI retrieval tasks.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

D-NOVA accelerates RAG with in-storage vector search · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Chang Eun Song, Sumukh Pinge, Tianqi Zhang, Sung Eun Kim, Tajana S. Rosing, Mingu Kang ·

    D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation

    arXiv:2607.17538v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances the factual grounding of large language model (LLM) inference by retrieving relevant information from external knowledge bases. However, its dense vector retrieval introduces significa…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Mingu Kang ·

    D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation

    Retrieval-Augmented Generation (RAG) enhances the factual grounding of large language model (LLM) inference by retrieving relevant information from external knowledge bases. However, its dense vector retrieval introduces significant latency and energy overhead, becoming the prima…

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