  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.
