{"ID":2845783,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03437","arxiv_id":"2511.03437","title":"HERP: Hardware for Energy Efficient and Realtime DB Search and Cluster Expansion in Proteomics","abstract":"Database search and clustering are fundamental components of many data analytics problems, such as mass spectrometry-driven proteomics. Traditional full clustering and search algorithms suffer from high resource usage and long latencies. We introduce HERP, a lightweight incremental clustering method and a highly parallelizable database (DB) search platform that utilizes 3T2MTJ SOT-MRAM based CAM in 7nm technology for in-memory acceleration. A single hardware initialization using pre-clustered proteomics data allows for continuous DB searching and local re-clustering, providing a more practical and efficient alternative to clustering from scratch. Heuristics derived from the initial pre-clustered data guide the incremental process, accelerating clustering by 20x at a cost of 0.3% increase in clustering error where DB search results overlap by 96% with SOTA algorithms validating search quality. For a 131GB human genome proteomics dataset HERP setup requires 1.19mJ for 2M spectra while 1000 query search consumes only 1.1uJ at SOTA accuracy. Bucket-wise parallelization and query scheduling provides additional 100x speedup.","short_abstract":"Database search and clustering are fundamental components of many data analytics problems, such as mass spectrometry-driven proteomics. Traditional full clustering and search algorithms suffer from high resource usage and long latencies. We introduce HERP, a lightweight incremental clustering method and a highly parall...","url_abs":"https://arxiv.org/abs/2511.03437","url_pdf":"https://arxiv.org/pdf/2511.03437v2","authors":"[\"Md Mizanur Rahaman Nayan\",\"Zheyu Li\",\"Flavio Ponzina\",\"Sumukh Pinge\",\"Tajana Rosing\",\"Azad J. Naeemi\"]","published":"2025-11-05T12:55:35Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.ET\"]","methods":"[]","has_code":false}