Year: 2024
Type: conference-paper
Abstract: Mass spectrometry (MS) is essential for protein analysis but faces significant challenges with large datasets and complex post-translational modifications, resultingin difficulties in spectral identification. Open Modification Search (OMS) improves the analysis of these modifications. We present RapidOMS, a solution leveraging the Samsung SmartSSD, which integrates SSD and FPGA in a near-storage configuration to minimize data movement and enhance the efficiency of large-scale database searching. RapidOMS employs hyperdimensional computing (HDC), a brain-inspired, high-dimensional data processing approach, exploiting the parallel processing and low-latency capabilities of FPGAs, making it well-suited for MS. Utilizing the parallelism and efficiency of bitwise operations in HDC, RapidOMS delivers up to a 60x speedup over the state-of-the-art (SOTA) CPU tool ANN-Solo and is 2.72x faster than the GPU tool HyperOMS. Furthermore, RapidOMS achieves an 11x improvement in energy efficiency compared to conventional systems, providing scalable, energy-efficient solutions for large-scale proteomics applications and advancing the efficient processing of proteomic data.
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Awards: FET: Small: Brain-Inspired Hyperdimensional Computing for IoT Applications, AI Institute for Learning-Enabled Optimization at Scale (TILOS), Category II: A Prototype National Research Platform, IUCRC Phase I University of California, San Diego: Center for Power Management Integration (PMIC), Collaborative Research: MLWiNS: Hyperdimensional Computing for Scalable IoT Intelligence Beyond the Edge, Collaborative Research: IIS: RI: Medium: Lifelong learning with hyper dimensional computing, CCRI: ENS: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)
OpenAlex ID: W4405709954