MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem

2025-06-12

AI Summary

Rapid advances in sequencing technologies have exacerbated I/O bottlenecks and compute-memory speed mismatches in Raw Signal Genomic Analysis (RSGA). To address this, we propose a storage-centric RSGA acceleration architecture that unifies Processing-Near-Memory and Processing-Using-Memory paradigms. Our approach introduces the first in-memory pipelined RSGA engine featuring dual-stage filtering and low-precision quantization, integrated with DRAM-based in-memory computing, programmable flash controller acceleration, and adaptive signal preprocessing. Through tight hardware-software co-design, our architecture achieves average speedups of 93× over a pure-software baseline and 40× over the state-of-the-art hardware accelerator, while reducing energy consumption by 427× and 72×, respectively. These improvements significantly enhance end-to-end real-time performance and energy efficiency for RSGA workloads.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Raw signal genome analysis (RSGA) has emerged as a promising approach to enable real-time genome analysis by directly analyzing raw electrical signals. However, rapid advancements in sequencing technologies make it increasingly difficult for software-based RSGA to match the throughput of raw signal generation. This paper demonstrates that while hardware acceleration techniques can significantly accelerate RSGA, the high volume of genomic data shifts the performance and energy bottleneck from computation to I/O data movement. As sequencing throughput increases, I/O overhead becomes the main contributor to both runtime and energy consumption. Therefore, there is a need to design a high-performance, energy-efficient system for RSGA that can both alleviate the data movement bottleneck and provide large acceleration capabilities. We propose MARS, a storage-centric system that leverages the heterogeneous resources within modern storage systems (e.g., storage-internal DRAM, storage controller, flash chips) alongside their large storage capacity to tackle both data movement and computational overheads of RSGA in an area-efficient and low-cost manner. MARS accelerates RSGA through a novel hardware/software co-design approach. First, MARS modifies the RSGA pipeline via two filtering mechanisms and a quantization scheme, reducing hardware demands and optimizing for in-storage execution. Second, MARS accelerates the RSGA steps directly within the storage by leveraging both Processing-Near-Memory and Processing-Using-Memory paradigms. Third, MARS orchestrates the execution of all steps to fully exploit in-storage parallelism and minimize data movement. Our evaluation shows that MARS outperforms basecalling-based software and hardware-accelerated state-of-the-art read mapping pipelines by 93x and 40x, on average across different datasets, while reducing their energy consumption by 427x and 72x.

Problem

• Addressing I/O bottlenecks in raw signal genome analysis
• Reducing data movement and computational overheads in RSGA
• Enabling high-performance, energy-efficient in-storage genome processing

Innovation

• In-storage processing for genome analysis acceleration
• Hardware/software co-design with filtering and quantization
• Leverages Processing-Near-Memory and Processing-Using-Memory