Cross-Domain Acceleration of Open Modification Search: From Commodity Platforms to Emerging Memory and Storage Devices
2026-07-20 • Hardware Architecture
Hardware ArchitectureEmerging TechnologiesPerformance
AI summaryⓘ
The authors studied different hardware platforms to speed up a data-heavy process in mass spectrometry called open modification search (OMS). They found that moving reference data, not computing, limits performance. By using a special computing method based on binary hyperdimensional computing, they made OMS work well even on devices with hardware imperfections. Their experiments showed that memory- and storage-focused architectures can greatly speed up OMS and use much less energy compared to other platforms.
mass spectrometryopen modification searchbinary hyperdimensional computingmemory-centric architecturestorage-centric architectureGPUFPGAin-memory processing3D NANDenergy efficiency
Authors
Sumukh Pinge, Chang Eun Song, Po-Kai Hsu, Zheyu Li, Ashkan Moradifirouzabadi, Yanru Chen, Xiangjin Wu, Wei-Chen Chen, Eric Pop, Shimeng Yu, H. -S. Philip Wong, Tajana Rosing, Mingu Kang
Abstract
Open modification search (OMS) in mass spectrometry (MS) is a data-intensive workload whose performance is dominantly limited by reference data movement rather than computation. Prior OMS accelerators have largely been evaluated in isolation, making it difficult to understand system-level trade-offs across platforms. This paper presents the first workload-driven, cross-platform survey of accelerators for MS search by studying not only commodity platforms, but also emerging memory- and storage-centric architectures, including GPUs, near-storage FPGAs, DRAM near-memory processing, ReRAM/PCM in-memory processing, and 3D NAND/FeNAND in-storage processing, under consistent algorithmic and accuracy assumptions. Leveraging a binary hyperdimensional computing (HDC)-based OMS formulation that reduces similarity evaluation to lightweight bitwise primitives and tolerates device-level non-idealities, we enable a robust execution on memory-centric architectures despite device-level non-idealities and limited computing capability. Overall, this study identifies memory- and storage-centric architectures as a key architectural breakthrough for large-scale, high-speed search acceleration, delivering up to >100x speedup and >40,000x improvement in energy efficiency.