2024/11/14 by Keming Fan, Ashkan Moradifirouzabadi, Fan, Keming +15 · 2 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Advanced Data Storage Technologies #Advanced NMR Techniques and Applications #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Electrical engineering #Hardware Architecture (cs.AR) #Metabolomics and Mass Spectrometry Studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.09760
openalex publication_date 2024/11/14 · openalex created_date 2024/11/21 · openalex updated_date 2026/07/28
Mass spectrometry (MS) is essential for proteomics and metabolomics but faces impending challenges in efficiently processing the vast volumes of data. This paper introduces SpecPCM, an in-memory computing (IMC) accelerator designed to achieve substantial improvements in energy and delay efficiency for both MS spectral clustering and database (DB) search. SpecPCM employs analog processing with low-voltage swing and utilizes recently introduced phase change memory (PCM) devices based on superlattice materials, optimized for low-voltage and low-power programming. Our approach integrates contributions across multiple levels: application, algorithm, circuit, device, and instruction sets. We leverage a robust hyperdimensional computing (HD) algorithm with a novel dimension-packing method and develop specialized hardware for the end-to-end MS pipeline to overcome the non-ideal behavior of PCM devices. We further optimize multi-level PCM devices for different tasks by using different materials. We also perform a comprehensive design exploration to improve energy and delay efficiency while maintaining accuracy, exploring various combinations of hardware and software parameters controlled by the instruction set architecture (ISA). SpecPCM, with up to three bits per cell, achieves speedups of up to 82x and 143x for MS clustering and DB search tasks, respectively, along with a four-orders-of-magnitude improvement in energy efficiency compared with state-of-the-art CPU/GPU tools.