Open Modification Search (OMS) is a promising algorithm for mass spectrometry analysis that enables the discovery of modified peptides. However, OMS encounters challenges as it exponentially extends the search scope. Existing OMS accelerators either have limited parallelism or struggle to scale effectively with growing data volumes. In this work, we introduce an OMS accelerator utilizing multi-level-cell (MLC) RRAM memory to enhance storage capacity by 3x. Through in-memory computing, we achieve up to 77x faster data processing with two to three orders of magnitude better energy efficiency. Testing was done on a fabricated MLC RRAM chip. We leverage hyperdimensional computing to tolerate up to 10% memory errors while delivering massive parallelism in hardware.

翻译：开放修饰搜索（OMS）是一种用于质谱分析的前沿算法，能够发现修饰肽段。然而，OMS面临搜索范围呈指数级扩展的挑战。现有OMS加速器要么并行能力有限，要么难以随数据量增长有效扩展。本研究提出一种基于多级存储单元（MLC）RRAM存储器的OMS加速器，将存储容量提升3倍。通过存内计算技术，数据处理速度可提升达77倍，同时能效提高两到三个数量级。实验基于已流片的MLC RRAM芯片进行测试。我们利用超维计算技术，在硬件中实现大规模并行处理的同时，可容忍高达10%的存储错误。