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（MS）是蛋白质组学和代谢组学的关键技术，但在高效处理海量数据方面面临日益严峻的挑战。本文提出SpecPCM，一种内存计算（IMC）加速器，旨在显著提升MS谱图聚类和数据库（DB）搜索的能量与延迟效率。SpecPCM采用低电压摆幅的模拟处理方式，并利用基于超晶格材料、针对低电压低功耗编程优化的新型相变存储器（PCM）器件。我们的方法整合了应用、算法、电路、器件及指令集等多个层面的创新贡献。我们采用一种鲁棒的超维度计算（HD）算法，结合新颖的维度打包方法，并开发了针对端到端MS流程的专用硬件，以克服PCM器件的非理想特性。我们进一步通过使用不同材料，针对不同任务优化了多级PCM器件。我们还进行了全面的设计探索，在保持精度的同时提升能量与延迟效率，探索了由指令集架构（ISA）控制的各种硬件与软件参数组合。SpecPCM支持每个单元最多三位存储，在MS聚类和DB搜索任务上分别实现了高达82倍和143倍的加速，同时与最先进的CPU/GPU工具相比，能效提升了四个数量级。