Presentation
M²CAM: A Multi-Level Memristor-Based Self-Adaptive CAM Architecture for Genome Processing Acceleration
DescriptionGenomic analysis workflows, such as single-cell RNA sequencing (scRNA-seq), demand massive sequence matching and classifications, yet are fundamentally limited by the data movement overhead and bandwidth bottleneck of von Neumann architectures. Although in-memory computing (IMC) has emerged as a promising solution, most existing IMC-based genome accelerators rely on digital encoding schemes, which introduce excessive hardware overhead and limit processing efficiency. This work presents a Multi-level Memristor-based Self-adaptive CAM (content addressable memory) Architecture (M²CAM) for genome processing acceleration. Unlike conventional digital approaches, the proposed design leverages analog CAM encoding based on multi-level memristor conductance states, enabling compact representation of nucleotides with significantly reduced device count and improved parallelism. Furthermore, a self-adaptive error correction mechanism dynamically adjusts matching precision through hierarchical operation modes, ensuring robust sequence matching under device variations. A parallelized genome classification framework is developed to demonstrate the system's efficiency, using single-cell RNA sequencing as a representative application. Experimental results show that the proposed architecture achieves a 50%~75% reduction in device usage, while on real scRNA-seq datasets, M²CAM delivers 131.2×~3088.9× and 161.2×~269.6× improvements in energy consumption and latency, compared with CPU/GPU and traditional bioinformatics tools (e.g., STAR).
Event Type
Research Manuscript
TimeWednesday, July 2912:15pm - 12:30pm PDT
LocationMtg Room 203AB
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