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

TL;DR

This paper presents a novel OMS accelerator using multi-level-cell RRAM memory and hyperdimensional computing, achieving significant speed and energy efficiency improvements for mass spectrometry peptide analysis.

Contribution

The work introduces a scalable OMS accelerator with enhanced storage and processing capabilities leveraging MLC RRAM and in-memory computing techniques.

Findings

Up to 3x increased storage capacity with MLC RRAM.

Achieves 77x faster data processing and 2-3 orders of magnitude better energy efficiency.

Supports up to 10% memory errors using hyperdimensional computing.

Abstract

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.