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.

翻译：质谱分析在蛋白质组学和代谢组学中至关重要，但面临海量数据处理效率的严峻挑战。本文提出SpecPCM——一种面向质谱谱聚类和数据库搜索的内存计算加速器，可显著提升能效与延迟效率。该加速器采用低电压摆幅的模拟处理方式，基于近期提出的超晶格材料相变存储器器件，针对低电压低功耗编程进行优化。我们的方法在应用、算法、电路、器件及指令集等多个层面实现协同创新：利用鲁棒的类脑超维计算算法与新型维度打包方法，为端到端质谱分析流水线开发专用硬件以克服相变存储器器件的非理想特性；通过采用不同材料优化多级相变存储器器件以适应不同任务；并对硬件/软件参数组合（由指令集架构控制）进行全面的设计空间探索，在保持精度的同时提升能效与延迟效率。SpecPCM（每单元存储最多三比特）在质谱聚类和数据库搜索任务中分别实现了高达82倍和143倍的加速，相较于当前最优的CPU/GPU工具，能效提升达四个数量级。