Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.

翻译：基于质谱的蛋白质组学是个性化医疗的关键推动技术，能够深入解析生物系统中复杂的蛋白质组成。该技术在生物技术和生物医学领域具有广泛应用，但面临显著的计算瓶颈。现有方法处理大规模数据集通常需要数小时甚至数天，尤其在谱聚类领域。为解决这些效率问题，我们提出SpecHD——一种超维计算（HDC）框架，并结合FPGA加速架构与近存储预处理。通过利用HDC环境中的简化二进制运算，SpecHD充分发挥FPGA的低延迟与并行处理能力。该方法显著提升了聚类速度与效率，为未来医疗应用中的实时高通量数据分析奠定基础。评估表明，SpecHD在维持甚至超越现有聚类质量指标的同时，大幅缩短计算时间。具体而言，它能在5分钟内完成包含2500万个MS/MS谱图与131 GB质谱数据的大规模人类蛋白质组数据集聚类。相比现有最优方案，SpecHD能效提升超过31倍，加速比达6至54倍，成为快速分析质谱数据、推动个性化医疗应用的极具前景的方案。