纳米孔感知嵌入式检测用于移动DNA测序:一个Viterbi-HMM设计与深度学习方法对比.
Karim Hammad1,2, Zhongpan Wu2, Ebrahim Ghafar-Zadeh2
1Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, Egypt.
Biosensors
|September 26, 2025
概括
我们在RISC-V处理器上使用隐藏马尔科夫模型 (HMM) 开发了一种节能硬件软件系统,用于DNA测序基调. 这种方法可显著降低移动生物传感应用的功耗.
科学领域:
- 生物技术是生物技术.
- 基因组学就是基因组学.
- 嵌入式系统工程 嵌入式系统工程
背景情况:
- 纳米孔DNA测序提供实时诊断,但面临着移动使用的能源挑战.
- 深度学习 (DL) 模型用于基调是准确的,但耗电密集的,限制了便携式应用程序.
研究的目的:
- 为DNA序列检测提出一个节能硬件软件框架.
- 在定制的RISC-V核心上评估基于Viterbi的隐藏马尔科夫模型 (HMM) 探测器,与基于DL的方法对比.
主要方法:
- 在64位RISC-V核心和Virtex-7FPGA上实现了基于Viterbi的HMM探测器.
- 评估能源效率和推断准确度与商品加工商和最先进的DL基调器相比.
主要成果:
- 在x86,ARM和火箭系统上实现了比HMM高6.5倍,5.5倍和4.6倍的能源效率.
- 证明了15倍和2.4倍的能源效率优于DL探测器,具有竞争力的准确性.
结论:
- 经典的概率算法,如HMMs,当与轻量级嵌入式处理器集成时,提供了实际的优势.
- 拟议的框架适用于能源受限制的生物传感,使得基于现场的基因组监测和护理点诊断成为可能.
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