{"ID":5438840,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T12:10:16.449663231Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31554","arxiv_id":"2606.31554","title":"In-situ Indexing via Memristive Content-Addressable Memory","abstract":"Processing-in-Memory (PIM) is a proven paradigm for overcoming the ``memory wall\". However, while data indexing is severely bottlenecked by this same wall, it remains unclear how indexing can effectively benefit from PIM's unique capabilities. We present PATH, an in-situ indexing architecture that bridges this gap by leveraging the massive parallelism and inherent data-movement of PIMs. Specifically, we first reformulate the fundamental indexing operations, namely Insert, Search, Update, and Delete, into highly parallel in-situ content-addressable memory operations executed directly within memory arrays. Taking hash indexes as a typical case, we elaborate how PATH breaks the inherent trade-off among memory accesses, load factor, and process latency in conventional hashing schemes. By adopting ultra-large logical buckets and in-memory moving, PATH virtually eliminates the cost of hash collision resolution and significantly reduces resizing overhead. Compared with state-of-the-art schemes, PATH achieves $4.7-7.8\\times$ higher throughput, $\u003e14.5\\times$ lower tail latency, and $\u003e61.4\\%$ fewer memory accesses under insertions, laying a scalable foundation for next-generation data-centric computing.","short_abstract":"Processing-in-Memory (PIM) is a proven paradigm for overcoming the ``memory wall\". However, while data indexing is severely bottlenecked by this same wall, it remains unclear how indexing can effectively benefit from PIM's unique capabilities. We present PATH, an in-situ indexing architecture that bridges this gap by l...","url_abs":"https://arxiv.org/abs/2606.31554","url_pdf":"https://arxiv.org/pdf/2606.31554v1","authors":"[\"Bing Wu\",\"Xueliang Wei\",\"Shiyi Song\",\"Yibo Liu\",\"Jinpeng Liu\",\"Wei Tong\",\"Hao Tong\",\"Yuchong Hu\",\"Dan Feng\"]","published":"2026-06-30T12:11:26Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.ET\"]","methods":"[]","has_code":false}