Database (DB) search and clustering are fundamental to data-analytics workloads such as mass spectrometry-driven proteomics. Current workflows rely on resource-intensive search and re-clustering, often offloaded to the cloud, raising privacy concerns for sensitive biological data. In this work, we enable efficient and privacy-preserving interaction on low-resource hardware platform through a cross-layer co-design approach. At the algorithm level, we propose a hardware-friendly data driven incremental update mechanism while preserving nearly the same proteomics outcome, eliminating costly repeated re-clustering during database updates. Performing cluster expansion directly during similarity search maps the algorithm naturally onto content-addressable memory (CAM), fusing distance computation and cluster expansion into one in-memory operation and eliminating dedicated distance units and data movement. This enables reduced footprint making a compact CAM-based accelerator feasible. We exploit bucket-wise search to scale and parallelize by mapping spectra buckets to CAM arrays to serve large dataset and maximize throughput, respectively. Bucket cache and main memory enables mapping datasets of different scales. Experimental results show a ~20x algorithmic speedup over re-clustering with only 0.3% additional error and 96% DB search overlap with state-of-the-art(SOTA) methods. Bucket-wise parallelization enables ~100x further acceleration over sequential search. The hardware implementation with 24.4MB SOT-MRAM CAM with 2.5MB bucket cache sustains 3.21uJ per 1K queries and 263.8uS maximum latency on a 131GB human proteome dataset.

翻译：暂无翻译