HERP: Hardware for Energy Efficient and Realtime DB Search and Cluster Expansion in Proteomics
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909919070912512 |
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| author | Nayan, Md Mizanur Rahaman Li, Zheyu Ponzina, Flavio Pinge, Sumukh Rosing, Tajana Naeemi, Azad J. |
| author_facet | Nayan, Md Mizanur Rahaman Li, Zheyu Ponzina, Flavio Pinge, Sumukh Rosing, Tajana Naeemi, Azad J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HERP: Hardware for Energy Efficient and Realtime DB Search and Cluster Expansion in Proteomics Nayan, Md Mizanur Rahaman Li, Zheyu Ponzina, Flavio Pinge, Sumukh Rosing, Tajana Naeemi, Azad J. Databases Emerging Technologies 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. |
| title | HERP: Hardware for Energy Efficient and Realtime DB Search and Cluster Expansion in Proteomics |
| topic | Databases Emerging Technologies |
| url | https://arxiv.org/abs/2511.03437 |