# Efficient Open Modification Spectral Library Searching in High-Dimensional Space with Multi-Level-Cell Memory

arXiv: https://arxiv.org/abs/2405.02756 · HTML: https://mlsanity.com/paper/2405.02756/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2024.

## Built on by (most cited first, 3 of 3)

- [QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design](https://mlsanity.com/paper/2601.14549/) (2026)
- [Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective](https://mlsanity.com/paper/2603.03830/) (2026)
- [FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing](https://mlsanity.com/paper/2508.12021/) (2025)

## Abstract (arXiv)

Open Modification Search (OMS) is a promising algorithm for mass spectrometry analysis that enables the discovery of modified peptides. However, OMS encounters challenges as it exponentially extends the search scope. Existing OMS accelerators either have limited parallelism or struggle to scale effectively with growing data volumes. In this work, we introduce an OMS accelerator utilizing multi-level-cell (MLC) RRAM memory to enhance storage capacity by 3x. Through in-memory computing, we achieve up to 77x faster data processing with two to three orders of magnitude better energy efficiency. Testing was done on a fabricated MLC RRAM chip. We leverage hyperdimensional computing to tolerate up to 10% memory errors while delivering massive parallelism in hardware.