2025/11/05 by Nayan, Md Mizanur Rahaman, Li, Zheyu, Ponzina, Flavio +3
Chemistry · Computer Science · #Advanced Proteomics Techniques and Applications #Algorithms and Data Compression #Databases (cs.DB) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Parallel Computing and Optimization Techniques
paper · doi:10.48550/arxiv.2511.03437
openalex publication_date 2025/11/05 · openalex created_date 2025/11/07 · openalex updated_date 2026/07/28
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.